Fuzzy Logic and Neural Network Handbook
McGraw-Hill, 1996 - Computers - 700 pages
Fuzzy logic and neural networks, which are linked, have emerged as the keystone of developing intelligent computer systems with broad application throughout engineering and science. Neural nets allow them to better manage uncertainty. That combination gives the ability to solve many classes of problems more efficiently. This book deals with principles and algorithms, applications and architectures and systems.
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Learning Categorization Rule Formation and Prediction
JeanPaul Haton Universite Henri Poincare CRININRIANancy France chap
Reactive Control Using Fuzzy Logic Enrique H Ruspini 24 1
57 other sections not shown
activation function adaptive algorithm annealing neural network application approach approximate reasoning architecture backpropagation basis functions centroidal clustering computational constraint path convergence convex data set defined defuzzification degree of membership density deterministic annealing neural dynamic error estimate example FAMM feedforward FIGURE FMFs Fuzzy ARTMAP fuzzy classifier fuzzy logic fuzzy relational equation fuzzy rules fuzzy sets fuzzy system gaussian Grossberg hidden layer hidden neurons hyperplanes IEEE IEEE Transactions if-part input space input variables iteration learning license plate recognition linear mapping matrix membership functions method minimal mutual information neural models neurons nodes nonlinear operator optimization problems output parameter pattern recognition percent performance possible worlds prediction probabilistic probability programming recognition rate recurrent neural networks regression samples shown in Fig solution sonar statistical supervised learning techniques test data then-part set theorem tion training data units walk function weights