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.
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Number of pages: 300
Weight: 498 g
Dimensions: 235 x 155 x 17 mm
Edition: Softcover reprint of hardcover 1st ed. 2007