Artificial Neural Networks: Theory and Applications
This comprehensive tutorial on artifical neural networks covers all the important neural network architectures as well as the most recent theory--e.g., pattern recognition, statistical theory, and other mathematical prerequisites. A broad range of applications is provided for each of the architectures.
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activation functions ADALINE adaptive annealing applications approximation associative memory attractor backpropagation behavior binary Boltzmann machine cells chaotic chapter classification computed connections convergence defined Delta Rule denote derivative described dimension dynamics entropy equation error surface estimate feedback feedforward feedforward networks forecasting fuzzy set given gradient descent Hebbian Hebbian learning hidden layer hidden units Hopfield network illustrated in Figure initial input pattern input vector learning algorithm learning process linear Lorenz attractor Lyapunov exponents mapping methods minimize MLFF networks multilayer mutual information n-dimensional neocognitron network architecture neural network neurons nonlinear number of hidden operation optimization output nodes output units parameter perceptron performance predict problem random recurrent networks sample sigmoid sigmoid function signal simulated simulated annealing SOFM solution space supervised learning target training examples training patterns training set types variables weight matrix weight values Widrow zero