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Computational Intelligence: A Methodological Introduction (Texts in Computer Science) 🔍
Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher (auth.) Springer-Verlag London, Texts in Computer Science, Texts in Computer Science, 2, 2016
英语 [en] · PDF · 15.7MB · 2016 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save
描述
This authoritative 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 second edition to the definitive textbook on Computational Intelligence has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs. Topics and features: Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools 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 This accessible text is an essential reference for students of artificial intelligence and intelligent systems, and a valuable resource for all researchers and practitioners seeking a self-study primer on computational intelligence. Rudolf Kruse and Sanaz Mostaghim are professors at the Department of Computer Science of the Otto von Guericke University of Magdeburg, Germany. Christian Borgelt is a principal researcher, and Christian Braune is a research assistant at the same institution. Matthias Steinbrecher is with SAP SE, Potsdam, Germany
备用文件名
lgrsnf/K:\!genesis\!repository8\sp\10.1007%2F978-1-4471-7296-3.pdf
备用文件名
nexusstc/Computational Intelligence: A Methodological Introduction/e7f44a5fa6ed1d7e202660b0666f4390.pdf
备用文件名
scihub/10.1007/978-1-4471-7296-3.pdf
备用文件名
zlib/Computers/Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher (auth.)/Computational Intelligence: A Methodological Introduction_2802132.pdf
备选作者
Kruse, Rudolf; Borgelt, Christian; Braune, Christian; Mostaghim, Sanaz; Steinbrecher, Matthias; Klawonn, Frank; Moewes, Christian
备选作者
Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher, Frank Klawonn, Christian Moewes
备用出版商
Springer London : Imprint: Springer
备用出版商
Vieweg+Teubner-Verlag
备用出版商
Springer London Ltd
备用版本
Texts in computer science, Second edition, London, United Kingdom, 2016
备用版本
Texts in Computer Science, 2nd ed. 2016, London, 2016
备用版本
United Kingdom and Ireland, United Kingdom
备用版本
Sep 17, 2016
元数据中的注释
sm60947797
元数据中的注释
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元数据中的注释
Source title: Computational Intelligence: A Methodological Introduction (Texts in Computer Science)
备用描述
Computational intelligence (CI) encompasses a range of nature-inspired methods that exhibit intelligent behavior in complex environments.This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required.Topics and features:Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software toolsContains numerous examples and definitions throughout the textPresents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networksCovers the latest approaches, including ant colony optimization and probabilistic graphical modelsWritten by a team of highly-regarded experts in CI, with extensive experience in both academia and industryStudents of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI.
备用描述
Front Matter....Pages i-xiii
Introduction to Computational Intelligence....Pages 1-5
Front Matter....Pages 7-7
Introduction to Neural Networks....Pages 9-13
Threshold Logic Units....Pages 15-35
General Neural Networks....Pages 37-46
Multilayer Perceptrons....Pages 47-92
Radial Basis Function Networks....Pages 93-112
Self-organizing Maps....Pages 113-129
Hopfield Networks....Pages 131-157
Recurrent Networks....Pages 159-171
Mathematical Remarks for Neural Networks....Pages 173-180
Front Matter....Pages 181-181
Introduction to Evolutionary Algorithms....Pages 183-212
Elements of Evolutionary Algorithms....Pages 213-243
Fundamental Evolutionary Algorithms....Pages 245-297
Computational Swarm Intelligence....Pages 299-325
Front Matter....Pages 327-327
Introduction to Fuzzy Sets and Fuzzy Logic....Pages 329-359
The Extension Principle....Pages 361-367
Fuzzy Relations....Pages 369-382
Similarity Relations....Pages 383-393
Fuzzy Control....Pages 395-430
Fuzzy Data Analysis....Pages 431-456
Front Matter....Pages 457-457
Introduction to Bayes Networks....Pages 459-463
Elements of Probability and Graph Theory....Pages 465-491
Decompositions....Pages 493-505
Evidence Propagation....Pages 507-519
Learning Graphical Models....Pages 521-530
Belief Revision....Pages 531-539
Decision Graphs....Pages 541-551
Back Matter....Pages 553-564
开源日期
2016-11-20
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