Neural Networks: A Comprehensive FoundationThis book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts. |
Contents
Introduction | 1 |
Learning Process | 45 |
Correlation Matrix Memory | 90 |
Copyright | |
16 other sections not shown
Common terms and phrases
activation function adaptive applied approximation back-propagation algorithm back-propagation learning Boltzmann machine Chapter classification computation condition Conference on Neural content-addressable memory convergence correlation matrix cost function defined denote desired response distribution dynamical eigenvalue eigenvectors entropy equation error signal error surface expert networks feature map feedback feedforward feedforward network FIGURE filter follows fundamental memory Gaussian gradient Hebbian Hebbian learning hidden layer hidden neurons Hopfield network IEEE input layer input patterns input space input-output iterations learning algorithm learning process learning rule learning-rate parameter Liapunov function linear Linsker LMS algorithm multilayer perceptron mutual information neural network nodes noise nonlinear operation optimization output layer output neurons performance principal components principal components analysis probability probability density function problem RBF network recurrent network represents Section self-organizing shown in Fig signal-flow graph simulated annealing SOFM algorithm stable statistical stochastic supervised learning synaptic weight vector update VC dimension weight matrix zero