## Intelligent engineering systems through artificial neural networks: neural networks, fuzzy logic and evolutionary programming : proceedings of the Artificial Neural Networks in Engineering (ANNIE '96) conference, held November 10-13, 1996, in St. Louis, Missouri, U.S.A.. Smart engineering systems, Volume 6Proceedings of the Artificial Neural Networks in Engineering Conference, November 10-13, 1996, St Louis, Missouri. Intelligent Engineering Systems Through Artificial Neural Networks Volume 6, Smart Engineering Systems: Neural Networks, Fuzzy Logic and Evolutionary Programming (ANNIE'96). The quest for building systems that can function automatically has attracted serious attention in the field. Volume 6 of this highly successful series boasts the contribution of 168 papers from researchers from 20 countries. They examine the theory and applications of smart engineering systems, artificial neural networks, fuzzy logic, and evolutionary programming. The volume provides refereed versions of the latest developments in design and manufacturing engineering, including comprehensive coverage of: Artificial Neural Network Architecture; Fuzzy Networks and Systems; Evolutionary Programming; Pattern Recognition; Adaptive Control; Smart Engineering System Design. Author and subject indices are included for quick access to topics of interest. |

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### Contents

Dynamical Behavior of Artificial Neural Networks with Random Weights | 17 |

A Binary Three Layered Neural Network with Switched Error Perturbation | 35 |

A General AutoAssociative Memory with Sample Learning Capability | 51 |

Copyright | |

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### Common terms and phrases

adaptive alarm amacrine cells analysis application approach approximation architecture Artificial Neural Networks associative memory autonomous agents backpropagation behavior binary space bipolar cell classification combinatorial optimization complex Computer concept convergence in binary correct binary outputs correlation dimension data set decision defined dynamic eddy-current Engineering environment equation estimator evaluation expected new MAE extracted rules feedforward Figure Full-RE fuzzy logic Genetic Algorithms gray correlation Hamming distance hidden node hidden units Hopfield Hopfield networks initial input patterns input samples interaction iteration layer learning algorithm linear Lyapunov exponent MAE in region mapping matrix membership functions method minimal neurons noise nonlinear number of hidden number of training optimal paper parameters performance point from region prediction problem proposed pruning robot rule extraction rule sets sigmoidal function signal simulation step stochastic target technique test inputs training inputs training patterns update values variables VC dimension vector weights