Neural Networks: Computational Models and Applications presents
important theoretical and practical issues in neural networks,
including the learning algorithms of feed-forward neural networks,
various dynamical properties of recurrent neural networks,
winner-take-all networks and their applications in broad manifolds
of computational intelligence: pattern recognition, uniform
approximation, constrained optimization, NP-hard problems, and
image segmentation. The book offers a compact, insightful
understanding of the broad and rapidly growing neural networks
domain.
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