Recurrent Neural Networks
by Xiaolin Hu, P. Balasubramaniam
Publisher: InTech 2008
Number of pages: 400
The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim.
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by Raul Rojas - Springer
A general theory of artificial neural nets. The book starts with the simple nets, and shows how the models change when more general computing elements and net topologies are introduced. Suitable as a basis for university courses in neurocomputing.
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Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, etc.
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We all are fascinated by the phenomena of intelligent behavior, as generated by our own brains. As physicists we want to understand if there are some general principles that govern the dynamics of the neural circuits that underlie these phenomena.
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Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).