Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Optinformatics in Evolutionary Learning and Optimization
This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics.Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process.The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning.
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Networked and Distributed Predictive Control
Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.
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Distributed Cooperative Model Predictive Control of Networked Systems
This book is inspired by the development of distributed model predictive control of networked systems to save computation and communication sources. The significant new contribution is to show how to design efficient DMPCs that can be coordinated asynchronously with the increasing effectiveness of the event-triggering mechanism and how to improve the event-triggered DMPC for different requirements improvement of control performance, extension to interconnected networked systems, etc. The book is likely to be of interest to the persons who are engaged in researching control theory in academic institutes, the persons who go in for developing control systems in R&D institutes or companies, the control engineers who are engaged in the implementation of control algorithms, and people who are interested in the distributed MPC.
R 3,371.71
Distributed Sensor Networks
The best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for students or as research material for engineers, the book gives readers up-to-date, practical insight on all aspects of the field.Revised and expanded, this second edition incorporates contributions from many veterans of the DARPA ISO SENSIT program as well as new material from distinguished researchers in the field. Image and Sensor Signal Processing focuses on software issues and the history and future of sensor networks. The book also covers information fusion and power management.Readers of this book may also be interested in Distributed Sensor Networks, Second Edition: Sensor Networking and Applications (ISBN: 9781439862872).
R 4,212.06
New Optimization Algorithms and their Applications
New Optimization Algorithms and Applications: Atom-Based, Ecosystem-Based and Economics-Based presents the development of three new optimization algorithms - an Atom Search Optimization (ASO) algorithm, an Artificial Ecosystem-Based Optimization algorithm (AEO), a Supply Demand Based Optimization (SDO), and their applications within engineering. These algorithms are based on benchmark functions and typical engineering cases. The book describes the algorithms in detail and demonstrates how to use them in engineering. The title verifies the performance of the algorithms presented, simulation results are given, and MATLAB® codes are provided for the methods described. Over seven chapters, the book introduces ASO, AEO and SDO, and presents benchmark functions, engineering problems, and coding. This volume offers technicians and researchers engaged in computer and intelligent algorithm work and engineering with one source of information on novel optimization algorithms.
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Artificial Intelligence for Communications and Networks
This two-volume set LNICST 396 and 397 constitutes the post-conference proceedings of the Third EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 79 full papers were carefully reviewed and selected from 159 submissions. The papers are organized in topical sections on Artificial Intelligence in Wireless Communications and Satellite Communications; Artificial Intelligence in Electromagnetic Signal Processing; Artificial Intelligence Application in Wireless Caching and Computing; Artificial Intelligence Application in Computer Network.
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Integrated Distributed Intelligent Systems in Manufacturing
Intelligent Manufacturing is a new disciplinary field which applies computer science, artificial intelligence, mechanical engineering and systems science to industrial manufacturing processes. This book presents a new integration architecture for implementing real-time distributed intelligent manufacturing systems.
R 4,659.87
Proteinoid Proto-neural Networks
Proteinoid Proto-Neural Networks explores the emergence of electrical activity, spiking behavior, and adaptive responses, supported by electrochemical, optical, and acoustic studies that reveal proteinoid-based logic, memory, and learning. It demonstrates how proteinoids, which are synthetic polymers formed from amino acids under high temperature, self-organize into excitable microspheres and assemble into protoneural networks capable of unconventional computation. By blending experimental biophysics with neuromorphic modeling and unconventional computing theory, this monograph establishes proteinoid systems as a platform for wetware computing and the study of emergent intelligence.This book is perfectly suited for researchers in computer science, computer engineering, soft matter physics, novel materials, neuromorphic engineering and computing, as well as those exploring bio-inspired systems, unconventional computing architectures, and the physicochemical foundations of proto-neural behavior.
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6GN for Future Wireless Networks
This 2-volume set constitutes the proceedings of the 5th International Conference on 6G for Future Wireless Networks, 6GN 2022, held in Harbin, China, in December 2022.The 60 full papers were selected from 194 submissions and present the state of the art and practical applications of 6G technologies. The papers are arranged thematically in tracks as follows: Resource Allocation for 6G Networks; Security and Privacy for 6G Networks; Big data mining and pattern analysis techniques for 6G Networks; Artificial intelligent techniques for 6G Networks; Mobile Edge Computing for 6G Networks; Unmanned Aerial Vehicle Communication for 6G Networks.
R 3,521.42
Combinatorial Optimization and Applications
This book constitutes the refereed proceedings of the 9th International Conference on Combinatorial Optimization and Applications, COCOA 2015, held in Houston, TX, USA, in December 2015. The 59 full papers included in the book were carefully reviewed and selected from 125 submissions. Topics covered include classic combinatorial optimization; geometric optimization; network optimization; applied optimization; complexity and game; and optimization in graphs.
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Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the First International Conference on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001.The 45 revised full papers presented were carefully reviewed and selected from a total of 87 submissions. Also included are two tutorial surveys and two invited papers. The book is organized in topical sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.
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Optimization in Machine Learning and Applications
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
R 5,348.73
Algorithms for Continuous Optimization
The NATO Advanced Study Institute on "Algorithms for continuous optimiza tion: the state of the art" was held September 5-18, 1993, at II Ciocco, Barga, Italy. It was attended by 75 students (among them many well known specialists in optimiza tion) from the following countries: Belgium, Brasil, Canada, China, Czech Republic, France, Germany, Greece, Hungary, Italy, Poland, Portugal, Rumania, Spain, Turkey, UK, USA, Venezuela. The lectures were given by 17 well known specialists in the field, from Brasil, China, Germany, Italy, Portugal, Russia, Sweden, UK, USA. Solving continuous optimization problems is a fundamental task in computational mathematics for applications in areas of engineering, economics, chemistry, biology and so on. Most real problems are nonlinear and can be of quite large size. Devel oping efficient algorithms for continuous optimization has been an important field of research in the last 30 years, with much additional impetus provided in the last decade by the availability of very fast and parallel computers. Techniques, like the simplex method, that were already considered fully developed thirty years ago have been thoroughly revised and enormously improved. The aim of this ASI was to present the state of the art in this field. While not all important aspects could be covered in the fifty hours of lectures (for instance multiob jective optimization had to be skipped), we believe that most important topics were presented, many of them by scientists who greatly contributed to their development.
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Intelligent Agents for Telecommunication Applications
This book constitutes the refereed proceedings of the Second International Workshop on Intelligent Agents for Telecommunication Applications, IATA'98, held in Paris, France, in July 1998, in conjunction with the 1998 Agents World Conference.The book presents 17 revised full papers carefully selected for inclusion in the volume. The book is divided into topical sections on network architecture, network configuration and planning, network optimization, network management, agent-based architectures for service applications.
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Open Control Networks
Control networks span a wide range of application areas. These networks are put into action in the `Digital Home', industrial applications, commercial buildings, transportation systems, gas stations, security systems, and they are found in most instances where smart sensors and smart actuators are used to exchange information. The authors of this volume provide an overview of various control network protocols and discuss LonTalk® protocol, Neuron® chip, programming model, network structures, network management, interoperability between nodes, application profiles, development and maintenance tools, performance analysis, and standardization activities. Open Control Networks: LonWorks/EIA 709 Technology will be an important resource for advanced students of control systems and embedded systems, engineers designing distributed networks, systems designers and architects, and others developing smart buildings and intelligent transportation systems.
R 4,671.04
Responsible Graph Neural Networks
More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications.Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details.Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.
R 5,648.77
Combinatorial Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Symposium on Combinatorial Optimization, ISCO 2016, held in Vietri sul Mare, Italy, in May 2016. The 38 revised full papers presented in this book were carefully reviewed and selected from 98 submissions. They present original research on all aspects of combinatorial optimization, such as algorithms and complexity; mathematical programming; operations research; stochastic optimization; and graphs and combinatorics.
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R 519.00
Global Optimization in Action
In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints. In many cases of practical relevance, the optimization problem structure does not warrant the global optimality of local solutions; hence, it is natural to search for the globally best solution(s). Global Optimization in Action provides a comprehensive discussion of adaptive partition strategies to solve global optimization problems under very general structural requirements. A unified approach to numerous known algorithms makes possible straightforward generalizations and extensions, leading to efficient computer-based implementations. A considerable part of the book is devoted to applications, including some generic problems from numerical analysis, and several case studies in environmental systems analysis and management. The book is essentially self-contained and is based on the author's research, in cooperation (on applications) with a number of colleagues. Audience: Professors, students, researchers and other professionals in the fields of operations research, management science, industrial and applied mathematics, computer science, engineering, economics and the environmental sciences.
R 4,688.98
Edge Learning for Distributed Big Data Analytics
Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.
R 3,340.58
Artificial Intelligence and Neural Networks
This book constitutes the thoroughly refereed post-proceedings of the 14th Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN 2005, held in Izmir, Turkey, June 2005. The book presents 26 revised full papers categorized in topical sections on robotics, image processing, classification, learning theory and support vector machines, fuzzy neural networks, robotics, fuzzy logic, machine learning, engineering applications, and neural networks architecture.
R 1,547.26
Metaheuristic Optimization in Power Engineering
A metaheuristic is a higher-level procedure designed to find, generate, or select a heuristic or partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited computation capacity. Metaheuristics can often find good solutions with less computational effort than other algorithms. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation and EV charging, are an application for such methods. The new edition of Metaheuristic Optimization in Power Engineering in two volumes uses a MATLAB-based software package for testing and comparing methods, and includes several new and substantially revised and updated chapters. Volume 1 covers principles and key algorithms, such as genetic and swarm algorithms, gravitational and metaheuristic algorithms, power flow and power dispatch under consideration of renewable generation. Volume 2 focuses on power distribution networks, including power flow, voltage control and regulation, optimisation of generation placement and sizing, state estimation analysis. This reference for researchers and advanced students working on power system analysis and optimization offers an overview of metaheuristic optimization approaches to solving problems in modern power systems.
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Foundations & Principles of Distributed Manufacturing
The book presents a coherent description of distributed manufacturing, providing a solid base for further research on the subject as well as smart implementations in companies. It provides a guide for those researching and working in a range of fields, such as smart manufacturing, cloud computing, RFID tracking, distributed automation, cyber physical production and global design anywhere, manufacture anywhere solutions. Foundations & Principles of Distributed Manufacturing anticipates future advances in the fields of embedded systems, the Internet of Things and cyber physical systems, outlining how adopting these innovations could rapidly bring about improvements in key performance indicators, which could in turn generate competition pressure by rendering successful business models obsolete. In laying the groundwork for powerful theoretical models, high standards for the homogeneity and soundness of the suggested setups are applied. The book especially elaborates on the upcoming competition in online manufacturing operations and respective control procedures.By outlining encapsulation and evolving decision-making principles, Foundations & Principles of Distributed Manufacturing fully conceptualizes the view of manufacturing networks as sets of loosely coupled interacting smart factory objects. Moreover, the book provides concrete approaches to a number of future fields, where distributed manufacturing might be applied. Both researchers and professionals will profit from the authors’ broad experience in Distributed Manufacturing and Fractal Enterprise implementations, where they initiated and completed a number of successful research projects: within the global Intelligent Manufacturing Systems (IMS) scheme, within the European Research Area frameworks as well as national contexts, and both in industry and at leading research institutions. This background ensures well-founded theory on one hand and valuable practical results on the other in a fascinating area that is still under intensive research. Readers will acquire essential insights as well as useful guidance for categorizing and specifying extended distributed manufacturing solutions and their professional implementations.
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Intelligent Sensor Networks
Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperationMaintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.
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Network Design And Optimization For Smart Cities
This comprehensive reference text is a collection of important research findings on the latest developments in network modeling for optimization of smart cities. Such models can be used from outlining the fundamental concepts of urban development to the description and optimization of physical networks, such as power, water or telecommunications. Networks help us understand city economics and various aspects of human interactions within cities with particular applications in quality of life and the flow of people and goods. Finally, the natural environment and even the climate of cities can be modeled and managed as networks.
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Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Learning and Optimization, LION 10, which was held on Ischia, Italy, in May/June 2016.The 14 full papers presented together with 9 short papers and 2 GENOPT papers were carefully reviewed and selected from 47 submissions. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to new ideas and methods; challenges and opportunities in various application areas; general trends, and specific developments.
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Handbook on Modelling for Discrete Optimization
This book aims to demonstrate and detail the pervasive nature of Discrete Optimization. The handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It is done with an academic treatment outlining the state-of-the-art for researchers across the domains of the Computer Science, Math Programming, Applied Mathematics, Engineering, and Operations Research. The book utilizes the tools of mathematical modeling, optimization, and integer programming to solve a broad range of modern problems.
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Distributed Computer and Communication Networks
This book constitutes revised selected papers from the 24th International Conference on Distributed and Computer and Communication Networks, DCCN 2021, held in Moscow, Russia, in September 2021. Due to the COVID-19 pandemic the conference was held online. The 31 full papers along with the 4 short papers were carefully reviewed and selected from 151 submissions.The papers are organized in the following topical sections: computer and communication networks; analytical modeling of distributed systems, and distributed systems applications.
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Computational Intelligence for Wireless Sensor Networks
Computational Intelligence for Wireless Sensor Networks: Principles and Applications provides an integrative overview of the computational intelligence (CI) in wireless sensor networks and enabled technologies. It aims to demonstrate how the paradigm of computational intelligence can benefit Wireless Sensor Networks (WSNs) and sensor-enabled technologies to overcome their existing issues.This book provides extensive coverage of the multiple design challenges of WSNs and associated technologies such as clustering, routing, media access, security, mobility, and design of energy-efficient network operations.It also describes various CI strategies such as fuzzy computing, evolutionary computing, reinforcement learning, artificial intelligence, swarm intelligence, teaching learning-based optimization, etc. It also discusses applying the techniques mentioned above in wireless sensor networks and sensor-enabled technologies to improve their design.The book offers comprehensive coverage of related topics, including:Emergence of intelligence in wireless sensor networksTaxonomy of computational intelligenceDetailed discussion of various metaheuristic techniquesDevelopment of intelligent MAC protocolsDevelopment of intelligent routing protocolsSecurity management in WSNsThis book mainly addresses the challenges pertaining to the development of intelligent network systems via computational intelligence. It provides insights into how intelligence has been pursued and can be further integrated in the development of sensor-enabled applications.
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Advances in Combining Intelligent Methods
Complex problems usually cannot be solved by individual methods or techniques and require the synergism of more than one of them to be solved. This book presents a number of current efforts that use combinations of methods or techniques to solve complex problems in the areas of sentiment analysis, search in GIS, graph-based social networking, intelligent e-learning systems, data mining and recommendation systems. Most of them are connected with specific applications, whereas the rest are combinations based on principles. Most of the chapters are extended versions of the corresponding papers presented in CIMA-15 Workshop, which took place in conjunction with IEEE ICTAI-15, in November 2015. The rest are invited papers that responded to special call for papers for the book. The book is addressed to researchers and practitioners from academia or industry, who are interested in using combined methods in solving complex problems in the above areas.
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Industrial Networks and Intelligent Systems
This book constitutes the refereed proceedings of the 10th EAI International Conference on INISCOM 2024, held in Da Nang, Vietnam, during February 20–21, 2024.The 18 full papers included in this book were carefully reviewed and selected from 41 submissions. They were organized in topical sections as follows: industrial networks and intelligent systems; telecommunications systems and networks; information processing and data analysis; security and privacy.
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Testbeds and Research Infrastructures for the Development of Networks and Communications
This book constitutes the refereed post-conference proceedings of the 14th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communications, TridentCom 2019, held in December 2019 in Changsha, China. The 10 full papers were selected from 62 submissions and are grouped into three sessions: AI and Internet Computing; QoS, Reliability, Modeling and Testing; and Wireless, Networking and Multimedia Application.
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Distributed Computing
The International Symposium on DIStributed Computing (DISC) 2002 was held in Toulouse, France, on October 28-30, 2002. The realization of distributed s- tems on numerous fronts and the proliferation of the Internet continue to make the symposium highly important and relevant. There were 76 regular submissions to DISC this year, which were read and evaluated by program committee members assisted by external reviewers. Twen- four papers were selected by the program committee to be included in these proceedings. The quality of submissions was high, and the committee had to decline some papers worthy of publication. The best student paper award was selected from among regular submissions that were not co-authored by any program committee member, and was given to Yongqiang Huang for his contribution "Assignment-Based Partitioning in a Condition Monitoring System", co-authored with Hector Garcia-Molina.October 2002 Dahlia Malkhi Organizing Committee Mamoun Filali Philippe Mauran G'erard Padiou (Chair) Philippe Qu'einnec Anne-Marie Zerr (Secretary) Steering Committee Andr'e Schiper, Chair (EPFL, Switzerland) Michel Raynal, Vice-chair (Irisa, France) Maurice Herlihy (Brown University, USA) Dahlia Malkhi, Program Chair (Hebrew University of Jerusalem, Israel) Jennifer Welch (Texas A&M University, USA) Alex Shvartsman (University of Connecticut, USA) Shay Kutten (Technion, Israel) Program Committee Gustavo Alonso (ETH Zurich, Switzerland) Roberto Baldoni (University of Rome, Italy) Dave Bakken (Washington State University, USA) Paul Ezhilchelvan (University of Newcastle, UK) Christof Fetzer (AT&T Labs-Research, USA) Faith Fich (University of Toronto, Canada) Roy Friedman (Technion, Israel) Juan A.
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Theory and Engineering of Dependable Computer Systems and Networks
This book contains papers on selected aspects of dependability analysis in computer systems and networks, which were chosen for discussion during the 16th DepCoS-RELCOMEX conference held in Wrocław, Poland, from June 28 to July 2, 2021. Their collection will be a valuable source material for scientists, researchers, practitioners and students who are dealing with design, analysis and engineering of computer systems and networks and must ensure their dependable operation.Being probably the most complex technical systems ever engineered by man (and also—the most dynamically evolving ones), organization of contemporary computer systems cannot be interpreted only as structures built on the basis of (unreliable) technical resources. Their evaluation must take into account a specific blend of interacting people (their needs and behaviours), networks (together with mobile properties, cloud organization, Internet of Everything, etc.) and a large number of users dispersed geographically and constantly producing an unconceivable number of applications. Ever-growing number of research methods being continuously developed for dependability analyses apply the newest techniques of artificial and computational intelligence. Selection of papers in these proceedings illustrates diversity of multi-disciplinary topics which are considered in present-day dependability explorations.
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Advanced Wired and Wireless Networks
Advanced Wired and Wireless Networks brings the reader a sample of recent research efforts representative of advances in the areas of recognized importance for the future Internet, such as ad hoc networking, mobility support and performance improvements in advanced networks and protocols. Advanced Wired and Wireless Networks is structured to meet the needs of a professional audience in industry, as well as graduate-level students in computer science and engineering.
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Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction
This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.
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Metaheuristic Optimization via Memory and Evolution
Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.
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Intelligent Computing Theories and Application
This two-volume set of LNCS 13393 and LNCS 13394 constitutes - in conjunction with the volume LNAI 13395 - the refereed proceedings of the 18th International Conference on Intelligent Computing, ICIC 2022, held in Xi'an, China, in August 2022. The 209 full papers of the three proceedings volumes were carefully reviewed and selected from 449 submissions.This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications”. Papers focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
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Optimization and Learning via Stochastic Gradient Search
An introduction to gradient-based stochastic optimization that integrates theory and implementationThis book explains gradient-based stochastic optimization, exploiting the methodologies of stochastic approximation and gradient estimation. Although the approach is theoretical, the book emphasizes developing algorithms that implement the methods. The underlying philosophy of this book is that when solving real problems, mathematical theory, the art of modeling, and numerical algorithms complement each other, with no one outlook dominating the others.The book first covers the theory of stochastic approximation including advanced models and state-of-the-art analysis methodology, treating applications that do not require the use of gradient estimation. It then presents gradient estimation, developing a modern approach that incorporates cutting-edge numerical algorithms. Finally, the book culminates in a rich set of case studies that integrate the concepts previously discussed into fully worked models. The use of stochastic approximation in statistics and machine learning is discussed, and in-depth theoretical treatments for selected gradient estimation approaches are included.Numerous examples show how the methods are applied concretely, and end-of-chapter exercises enable readers to consolidate their knowledge. Many chapters end with a section on “Practical Considerations” that addresses typical tradeoffs encountered in implementation. The book provides the first unified treatment of the topic, written for a wide audience that includes researchers and graduate students in applied mathematics, engineering, computer science, physics, and economics.
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Intelligent Control Systems using Computational Intelligence Techniques
Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions. Intelligent Control Systems using Computational Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications.
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Deterministic Global Optimization
This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c. programming, and interval analysis.It also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the theoretical as well as from the empirical point of view. Furthermore, extensions of the prototype algorithm for multicriteria global optimization problems as well as mixed combinatorial optimization problems are considered. Numerical examples based on facility location problems support the theory. Applications of geometric branch-and-bound methods, namely the circle detection problem in image processing, the integrated scheduling and location makespan problem, and the median line location problem in the three-dimensional space are also presented.The book is intended for both researchers and students in the areas of mathematics, operations research, engineering, and computer science.
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Complex-Valued Neural Networks
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networksQuaternionic neural networksClifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
R 4,851.00
Distributed Energy Resources in Microgrids
Distributed Energy Resources in Microgrids: Integration, Challenges and Optimization unifies classically unconnected aspects of microgrids by considering them alongside economic analysis and stability testing. In addition, the book presents well-founded mathematical analyses on how to technically and economically optimize microgrids via distributed energy resource integration. Researchers and engineers in the power and energy sector will find this information useful for combined scientific and economical approaches to microgrid integration. Specific sections cover microgrid performance, including key technical elements, such as control design, stability analysis, power quality, reliability and resiliency in microgrid operation.
R 3,825.00
