Neural Networks: Computational Models and Applications (Hardcover)

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.