Artificial Neural Networks: Theory and Applications"This is a comprehensive text on neural networks with a good balance between theory and applications. All of the important network architectures and learning algorithms are covered with a presentation of the underlying theory follow by typical applications. The book consists of seven parts: Introduction-Background and Biological Inspiration, Early Neural Networks and Developments, Multilayer Feedforward Neural Networks an Backpropagation, Dynamic Recurrent and Stochastic Neural Networks, Other Neural Network Architectures, Networks Based on Unsupervised Learning, and a concluding chapter on Neuro-fuzzt Systems, Soft Computing, Genetic Algorithms, and Neuro-Logic Networks. The latest developments in network architectures and learning algorithms are covered with extensive coverage given to dynamic recurrent networks and multilayer perceptrons and backpropogation learning"--Back cover. |
Contents
Applications of Multilayer Feedforward Networks with | 8 |
Characteristics of Artificial Networks | 20 |
3 | 37 |
Copyright | |
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Common terms and phrases
activation functions ADALINE adaptive annealing applications associative memory attractor backpropagation behavior Bernard Widrow binary Boltzmann machine cells chapter classification computed connections convergence correlation defined Delta Rule derivative described dimension dynamics entropy equation error surface estimate F1 layer feedback feedforward feedforward networks fuzzy fuzzy set given gradient descent Hebbian learning hidden layer hidden units Hopfield network illustrated in Figure initial input layer input pattern input vector iterations Kohonen learning algorithm learning process linear mapping methods minimize MLFF networks neocognitron network architecture neural network neuro-fuzzy neurons nonlinear optimization output nodes output units parameter pattern recognition performance predict problem random recurrent networks sample sigmoid sigmoid function simulated simulated annealing single SOFM solution supervised learning target training examples training patterns training process training set update rule variables weight matrix weight values weight vector Widrow zero