Computational Intelligence PC ToolsComputational 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. |
Contents
Introduction | 3 |
Biological Basis for Evolutionary Computation | 9 |
Application Areas | 15 |
Copyright | |
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Common terms and phrases
activation values adaptive adjusted applications back-propagation behavior binary biological calculated Chapter chromosomes classification cluster component computational intelligence connection weights defined defuzzification described developed discussed diskette Equation evolution strategies evolutionary computation evolutionary computation tools evolutionary programming evolved 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 linear matrix maximum measure membership functions membership values Networks Council 1996 Neural Networks Council neuron nonlinear operations output PEs output variable paradigms parameters particle swarm optimization percent performance population member position presented probability problem processing element radial basis function random range represent ROC curve run file scheduling schemata selected sigmoid source code specified spikes string sum-squared error supervised learning test set tion weight vector