Computational intelligence PC tools
AP Professional, Sep 1, 1996 - Computers - 464 pages
Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. * * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.
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Neural Network Theory and Paradigms
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activation function activation values adjusted applications average back-propagation binary biological calculated Chapter chromosomes classification cluster component computational intelligence computational intelligence tools concept connection weights crossover defined defuzzification described developed discussed diskette Equation evolution strategies evolutionary computation evolutionary programming example explanation facility feedforward Figure fitness value fuzzy expert system fuzzy logic fuzzy membership functions fuzzy min-max fuzzy rules fuzzy set fuzzy systems genetic algorithms hidden layer hyperbox IEEE Neural Networks individual initial input pattern intelligence systems Iris data set Kohonen learning algorithm linear matrix membership functions membership values mutation Networks Council 1996 neural network tools Neural Networks Council neuron nonlinear operations output PEs paradigms parameters particle swarm optimization percent performance presented probability problem processing element radial basis function random range represent ROC curve run file scheduling selected sigmoid source code specified string sum-squared error supervised learning tion training set two-layer weight vector