## 1993 IEEE International Conference on Neural Networks, San Francisco, California, March 28-April 1, 1993 |

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### Contents

NEUROFURY SYSTEMS HYBRID SYSTEMS | 1 |

Multiple networks for function learning E Alpaydin | 9 |

A universal structure for artiﬁcial neural networks F Rauf and H M Ahned | 15 |

Copyright | |

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### Common terms and phrases

activation function adaptive application approach approximation architecture artiﬁcial neural networks backpropagation behavior cell classiﬁcation cluster Computer conﬁguration connections constraints convergence corresponding cortical columns cost function data set deﬁned deﬁnition Delta rule dynamic entropy equation error example feature map feedforward ﬁeld ﬁgure ﬁlter ﬁnal ﬁnd ﬁrst ﬁxed point fuzzy Gaussian Genetic Algorithms given gradient gradient descent hidden layer hidden units Hopf bifurcation Hopﬁeld identiﬁcation IEEE implementation initial input pattern input vector iterations leaming learning algorithm learning rate limit cycle linear matrix method modiﬁed multilayer multilayer perceptron neurons node noise nonlinear optimal output parameters pattems PCNN perceptron performance prediction problem proposed recurrent network recurrent neural network represent robot rule saccade samples self-organizing self-organizing map serializable shows sigmoid sigmoid function signal solution space speciﬁc step structure synaptic threshold tion training data training set updating variables weight vector zero