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【原创】Computational Intelligence_An Introduction.Wiley.2002[New]
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本资源来自于互联网，仅供学习研究之用，不可涉及任何商业用途，请在下载后24小时内删除。 著作权归原作者或出版社所有。未经发贴人conanwj许可，严禁任何人以任何形式转贴本文，违者必究！ Computational Intelligence: An Introduction Authors Andries P. Engelbrecht Publisher:Wiley Pub Date: 2002 Pages: 311 ISBN 0-470-84870-7 Preface Man has learned much from studies of natural systems, using what has been learned to develop new algorithmic models to solve complex problems. This book presents an introduction to some of these technological paradigms, under the umbrella of computational intelligence (CI). In this context, the book includes artificial neural networks, evolutionary computing, swarm intelligence and fuzzy logic, which are respectively models of the following natural systems: biological neural networks, evolution, swarm behavior of social organisms, and human thinking processes. Why this book on computational intelligence? Need arose from a graduate course, where students do not have a deep background of artificial intelligence and mathematics. Therefore the introductory nature, both in terms of the CI paradigms and mathematical depth. While the material is introductory in nature, it does not shy away from details, and does present the mathematical foundations to the interested reader. The intention of the book is not to provide thorough attention to all computational intelligence paradigms and algorithms, but to give an overview of the most popular and frequently used models. As such, the book is appropriate for beginners in the CI field. The book is therefore also applicable as prescribed material for a third year undergraduate course. In addition to providing an overview of CI paradigms, the book provides insights into many new developments on the CI research front (including material to be published in 2002) - just to tempt the interested reader. As such, the material is useful to graduate students and researchers who want a broader view of the different CI paradigms, also researchers from other fields who have no knowledge of the power of CI techniques, e.g. bioinformaticians, biochemists, mechanical and chemical engineers, economists, musicians and medical practitioners. The book is organized in five parts. Part I provides a short introduction to the different CI paradigms and a historical overview. Parts II to V cover the different paradigms, and can be presented in any order. Part II deals with artificial neural networks (NN), including the following topics: Chapter 2 introduces the artificial neuron as the fundamental part of a neural network, including discussions on different activation functions, neuron geometry and learning rules. Chapter 3 covers supervised learning, with an introduction to different types of supervised networks. These include feedforward NNs, functional link NNs, product unit NNs and recurrent NNs. Different supervised learning algorithms are discussed, including gradient descent, scaled conjugate gradient, LeapProg and particle swarm optimization. Chapter 4 covers unsupervised learning. Different unsupervised NN models are discussed, including the learning vector quantizer and self-organizing feature maps. Chapter 5 introduces radial basis function NNs which are hybrid unsupervised and supervised learners. Reinforcement learning is dealt with in chapter 6. Much attention is given to performance issues of supervised networks in chapter 7. Aspects that are included are measures of accuracy, analysis of performance, data preparation, weight initialization, optimal learning parameters, network architecture selection, adaptive activation functions and active learning. Part III introduces several evolutionary computation models. Topics covered include: an overview of the computational evolution process in chapter 8. Chapter 9 covers genetic algorithms, chapter 10 genetic programming, chapter 11 evolutionary programming, chapter 12 evolutionary strategies, chapter 13 differential evolution, chapter 14 cultural evolution, and chapter 15 covers coevolution, introducing both competitive and symbiotic coevolution. Part IV presents an introduction to two types of swarm-based models: Chapter 16 discusses particle swarm optimization and covers some of the new developments in particle swarm optimization research. Ant colony optimization is overviewed in chapter 17. Part V deals with fuzzy systems. Chapter 18 presents an introduction to fuzzy systems with a discussion on membership functions, linguistic variables and hedges. Fuzzy inferencing systems are explained in chapter 19, while fuzzy controllers are discussed in chapter 20. An overview of rough sets is given in chapter 21. The conclusion brings together the different paradigms and shows that hybrid systems can be developed to attack difficult real-world problems. Throughout the book, assignments are given to highlight certain aspects of the covered material and to stimulate thought. Some example applications are given where they seemed appropriate to better illustrate the theoretical concepts. Several Internet sites will be helpful as an additional. These include: ? http://citeseer.nj.nec.com/ which is an excellent search engine for Al-related publications; ? http://www.ics.uci.edu/~mlearn/MLRepository.html, a repository of data bases maintained by UCI; ? http://www.cs.toronto.edu/~delve/, another repository of benchmark problems. http://www.lirmm.fr/~reitz/copie/siftware.html, a source of commercial and free software. http://www.aic.nrl.navy.mil/~aha/research/machine-leaming.htiiil, a repository of machine learning resources http://dsp.jpl.nasa.gov/members/payman/swarm/, with resources on swarm intelligence. http://www.cse.dmu.ac.uk/~rij/fuzzy.html and http://www.austinlinks.com/Fuzzy/ with information on fuzzy logic. http://www.informatik.uni-stuttgart.de/ifi/fk/evolalg/, a repository for evolutionary computing. http://www.evalife.dk/bbase, another evolutionary computing and artificial life repository. http://news.alife.org/, a source for information and software on Artificial Life. 本资源共8个可选网络硬盘链接，16.96 MB，保质期2009-08-30。 ---------------------------------------------------------------------------- Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf Computational Intelligence_An Introduction.Andries P. Engelbrecht. Wiley. 2002.pdf ---------------------------------------------------------------------------- |
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