Fundamentals of Artificial Neural Networks
MIT Press, 1995 - 511 страница
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers.
Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references.
Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the "average learning equation" analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backprop and its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.
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Computational Capabilities of Artificial Neural Networks
Mathematical Theory of Neural Learning
Adaptive Multilayer Neural Networks I
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activation function adaptive approximation arbitrary architecture artificial neural networks associative memory assumed asymptotically autoassociative basins of attraction binary Boolean functions capable chapter classifier clustering computational Consider convergence criterion function derived dichotomies discrete-time eigenvalues eigenvector employing equilibrium error example feedforward neural fundamental memories Gaussian genetic algorithm given gradient descent gradient-descent Hebbian Hebbian learning hidden layer hidden units Hopfield Hopfield net incremental backprop initial input pattern input space input vector interconnected Iteration learning rate learning rule Liapunov function linearly separable local minima matrix method minimizing multilayer neural net nonlinear Note Oja's rule optimal output layer output unit parameters perceptron polynomial positive preceding problem prototype random RBF network realized receptive field recurrent region retrieval rule in Equation samples Section Show shown in Figure sigmoidal signal simulated annealing solution stable stochastic strings supervised learning term theorem threshold gate tion training set values weight vector zero