Neural computing: Theory and Practice
This book for nonspecialists clearly explains major algorithms and demystifies the rigorous math involved in neural networks. Uses a step-by-step approach for implementing commonly used paradigms.
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Fundamentals of Artificial Neural Networks
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activation function array artificial neural networks associative memory axon backpropagation binary biological brain calculated Cauchy Cauchy distribution cognitron column comparison layer competition region complex cell components configuration connection convergence counterpropagation network desired output dot product energy error example excitatory Figure firing follows Grossberg layer Hebbian learning hidden layer Hopfield Hopfield network human IEEE input layer input pattern input vector Kohonen layer Kohonen neuron lateral inhibition layer neuron linear matrix multiplication method modulator multiplied neocognitron network weights neurotransmitter objective function operation Optical neural networks output layer output of neuron output vector perceptron perform photodetector problem produce random recognition layer recognition-layer neuron recurrent networks response result row vector signal simple cell single-layer networks stable step stored pattern thereby threshold tion training algorithm training process training set training vectors vigilance weight adjustment weight change weight mask weight matrix weight vectors weighted sum Widrow zero