Neural networks : computational models and applications
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Bibliographic Information
Neural networks : computational models and applications
（Studies in computational intelligence, v. 53）
Springer, c2007
- :hbk.
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Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Feedforward Neural Networks and Training Methods.- New Dynamical Optimal Learning for Linear Multilayer FNN.- Fundamentals of Dynamic Systems.- Various Computational Models and Applications.- Convergence Analysis of Discrete Time RNNs for Linear Variational Inequality Problem.- Parameter Settings of Hopfield Networks Applied to Traveling Salesman Problems.- Competitive Model for Combinatorial Optimization Problems.- Competitive Neural Networks for Image Segmentation.- Columnar Competitive Model for Solving Multi-Traveling Salesman Problem.- Improving Local Minima of Columnar Competitive Model for TSPs.- A New Algorithm for Finding the Shortest Paths Using PCNN.- Qualitative Analysis for Neural Networks with LT Transfer Functions.- Analysis of Cyclic Dynamics for Networks of Linear Threshold Neurons.- LT Network Dynamics and Analog Associative Memory.- Output Convergence Analysis for Delayed RNN with Time Varying Inputs.- Background Neural Networks with Uniform Firing Rate and Background Input.
by "Nielsen BookData"