Neural Networks

Neural networks, based on simple adaptive models of living neurons, has shown itself able to tackle a very wide range of problems, particularly in conjunction with probabilistic reasoning and Bayesian belief networks using local computations. Similarly genetic algorithms and simulated annealing have been used for the solution of a large range of optimization problems. Complex tasks in areas such as machine vision, time-series analysis, robotics, control, cost analysis, and even share price and currency prediction are routinely handled by neural networks. They are also proving of importance in the development of mixed hybrid systems, which utilize other approaches to hard information processing techniques. Genetic algorithms, expert systems and Bayesian techniques are all being combined with neural network constructions to give ever-more powerful applications. Experience, can be shown to work acceptably well.
The book should be of interest to all those working in: adaptive information processing, particularly in the allied fields of computer science, electrical engineering, physics and mathematics; also those researching in the neurosciences and branches of psychology and philsophy, particularly those concerned with neural modelling should benefit from this book. Corporate users include IT specialists , production and control engineers, research and development departments, and consultants. There are two companion volumes to this book, "Probability Reasoning and Bayesian Belief Networks" and "Applications of Modern Heuristic Methods", which individually stand alone, but combined form a set treating a broad but integrated spectrum of techniques and tools for undertaking complex tasks.
 
Neural networks, based on simple adaptive models of living neurons, has shown itself able to tackle a very wide range of problems, particularly in conjunction with probabilistic reasoning and Bayesian belief networks using local computations. Similarly genetic algorithms and simulated annealing have been used for the solution of a large range of optimization problems. Complex tasks in areas such as machine vision, time-series analysis, robotics, control, cost analysis, and even share price and currency prediction are routinely handled by neural networks. They are also proving of importance in the development of mixed hybrid systems, which utilize other approaches to hard information processing techniques. Genetic algorithms, expert systems and Bayesian techniques are all being combined with neural network constructions to give ever-more powerful applications. Experience, can be shown to work acceptably well.
The book should be of interest to all those working in: adaptive information processing, particularly in the allied fields of computer science, electrical engineering, physics and mathematics; also those researching in the neurosciences and branches of psychology and philsophy, particularly those concerned with neural modelling should benefit from this book. Corporate users include IT specialists , production and control engineers, research and development departments, and consultants. There are two companion volumes to this book, "Probability Reasoning and Bayesian Belief Networks" and "Applications of Modern Heuristic Methods", which individually stand alone, but combined form a set treating a broad but integrated spectrum of techniques and tools for undertaking complex tasks.