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.