Computational Intelligence: A Methodological Introduction de Rudolf Kruse

Computational Intelligence: A Methodological Introduction: Texts in Computer Science

Autor Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, Matthias Steinbrecher

en Limba Engleză Paperback – 30 mar 2023

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms.

Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

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Specificații

ISBN-13: 9783030422295
ISBN-10: 3030422291
Pagini: 639
Ilustrații: XIV, 639 p. 324 illus., 42 illus. in color.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 0.99 kg
Ediția:3rd ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Texts in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Part I: Neural Networks.- Introduction.- Threshold Logic Units.- General Neural Networks.- Multi-Layer Perceptrons.- Radial Basis Function Networks.- Self-Organizing Maps.- Hopfield Networks.- Recurrent Networks.- Mathematical Remarks for Neural Networks.- Part II: Evolutionary Algorithms.- Introduction to Evolutionary Algorithms.- Elements of Evolutionary Algorithms.- Fundamental Evolutionary Algorithms.- Computational Swarm Intelligence.- Part III: Fuzzy Systems.- Fuzzy Sets and Fuzzy Logic.- The Extension Principle.- Fuzzy Relations.- Similarity Relations.- Fuzzy Control.- Fuzzy Data Analysis.- Part IV: Bayes and Markov Networks.- Introduction to Bayes Networks.- Elements of Probability and Graph Theory.- Decompositions.- Evidence Propagation.- Learning Graphical Models.- Belief Revision.- Decision Graphs.

Recenzii

“The book presents a thorough exposition of the main concepts of computational intelligence. … It is an interesting book that may serve very well a wide audience, providing material for researchers, students as well as people working in industry.” (Catalin Stoean, zbMATH 1500.68001, 2023)

“The authors have written Computational intelligence in such a way that it can serve as both a textbook and a helpful reference book for students and practitioners of computing science and related fields. The presentation is careful and friendly yet technically sound.” (Soubhik Chakraborty, Computing Reviews, March 7, 2023)

Notă biografică

Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.

Textul de pe ultima copertă

Computational intelligence comprises concepts, paradigms, algorithms, and implementations of systems that are intended to exhibit intelligent behavior in complex environments. It relies heavily on (at least) nature-inspired methods, which have the advantage that they tolerate incomplete, imprecise and uncertain knowledge and thus also facilitate finding solutions that are approximative, manageable and robust at the same time.

Fully updated, this new edition of the authoritative textbook provides a clear and logical introduction to Computational Intelligence, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. Rather than aim for completeness, the goal is to give a methodical introduction, supporting fundamental concepts and their implementations with explanation of the theoretical background of proposed problem solutions.

Topics and features:

Offers new material on deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms
Contains numerous classroom-tested examples and definitions
Discusses in detail the classical areas of artificial neural networks, fuzzy systems, evolutionary algorithms, and Bayes and Markov networks
Reviews the latest developments, including such topics as ant colony optimization and probabilistic graphical models
Provides supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools

This seminal textbook is primarily meant as a companion book for lectures on the covered topics in the area of computational intelligence. However, it is also eminently suitable as a guidebook for self-study by students and practitioners from industry and commerce.

Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.

Caracteristici

Written by a team of highly-regarded experts, with extensive experience in both academia and industry Provides supplementary material on module descriptions, lecture slides, exercises with solutions, and software tools Addresses deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms