Short description:

Neural Networks: Computational Models and Applications covers a wealth of 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. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.

Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily.

Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.

Long description:

Neural Networks: Computational Models and Applications covers a wealth of 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. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.

Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily.

Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.

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.