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TABLE OF CONTENTS continued
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DeMers K KreutzDelgado
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activation adaptive applied approach approximation Artificial Neural Networks binary cell character chromosome classification component constraints convergence defined delta rule dynamics energy function entropy epochs equation error function example feedforward Figure filter fuzzy gaussian global gradient descent hidden layer hidden neurons hidden nodes hidden units Hopfield Network hyperplane IEEE implementation improved initial input patterns input vector iterations learning algorithm learning rate learning rule linear matrix mean squared error method minimize minimum multilayer multilayer perceptron network architecture neurofilter neurons noise nonlinear number of hidden optimal output layer output units parameters pattern recognition perceptron performance pixel preprocessing problem propagation proposed pruning recognition rate recurrent region represent Rumelhart samples selected sequence shown sigmoid sigmoid function signal simulation solution space squared error structure subgoal supervised learning target technique training patterns training set transformation wavelet weight space weight vector zero