Computational Intelligence

Computational Intelligence: Principles, Techniques and Applications comprehensively presents both theories and applications of computational intelligence in clear and precise language. This reference and textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, telecommunications and robots. It contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. Emerging areas of computational intelligence such as artificial life, particle swarm optimization, et al. are treated with examples in this book.

First text on the subject that covers all aspects of computational intelligence First to provide both theory and applications of computational intelligence in a single volume in a lucid, precise and highly comprehensive style Covers fundamentals such as fuzzy logic, neurocomputing, genetic algorithms and belief networks in detail and presents applications in database, expert systems, computer networks, image understanding and robotics Contains many numerical examples to illustrate the concepts and homework problems with sufficient hints so that the students can solve them on their own Includes supplementary material: sn.pub/extras

Texte du rabat
The book Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of Computational Intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of Fuzzy Sets and Logic, Neural Networks, Evolutionary Computing and Belief Networks. The application areas include Fuzzy Databases, Fuzzy Control, Image Understanding, Expert Systems, Object Recognition, Criminal Investigation, Telecommunication Networks and Intelligent Robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. Emerging areas of Computational Intelligence such as artificial life, particle swarm optimization, artificial immune systems, fuzzy chaos theory, rough sets and granular computing have also been addressed with examples in this book. The book ends with a discussion on a number of open- ended research problems in Computational Intelligence. Graduate students interested to pursue their research in this subject will greatly be benefited with these problems.

Contenu
An Introduction to Computational Intelligence.- Fuzzy Sets and Relations.- Fuzzy Logic and Approximate Reasoning.- Fuzzy Logic in Process Control.- Fuzzy Pattern Recognition.- Fuzzy Databases and Possibilistic Reasoning.- to Machine Learning Using Neural Nets.- Supervised Neural Learning Algorithms.- Unsupervised Neural Learning Algorithms.- Competitive Learning Using Neural Nets.- Neuro-dynamic Programming by Reinforcement Learning.- Evolutionary Computing Algorithms.- Belief Calculus and Probabilistic Reasoning.- Reasoning in Expert Systems Using Fuzzy Petri Nets.- Fuzzy Models for Face Matching and Mood Detection.- Behavioral Synergism of Soft Computing Tools.- Object Recognition from Gray Images Using Fuzzy ADALINE Neurons.- Distributed Machine Learning Using Fuzzy Cognitive Maps.- Machine Learning Using Fuzzy Petri Nets.- Computational Intelligence in Tele-Communication Networks.- Computational Intelligence in Mobile Robotics.- Emerging Areas of Computational Intelligence.- Research Problems for Graduate Thesis and Pre-Ph D Preparatory Courses.