Synopses & Reviews
Synopsis
This volume brings together a collection of top international researchers in the field of artificial neural networks with the = common theme being an attempt to tackle the problem of complexity. The contributions range from more theoretical analyses of the neural network approach to a number of application-oriented articles which indicate the extent of the problem from a more practical viewpoint. The use of neural networks is a relatively new, but increasingly popular, approach to the problem of complexity. Dealing with Complexity is an extremely multi-disciplinary = examination of the above issues: although primarily intended for industrial/academic researchers, and postgraduate students working within computing science, it will also be of interest to anyone=20 working on relevant research projects or applications within the following fields: physics, mathematics and engineering.
Synopsis
In almost all areas of science and engineering, the use of computers and microcomputers has, in recent years, transformed entire subject areas. What was not even considered possible a decade or two ago is now not only possible but is also part of everyday practice. As a result, a new approach usually needs to be taken (in order) to get the best out of a situation. What is required is now a computer's eye view of the world. However, all is not rosy in this new world. Humans tend to think in two or three dimensions at most, whereas computers can, without complaint, work in n- dimensions, where n, in practice, gets bigger and bigger each year. As a result of this, more complex problem solutions are being attempted, whether or not the problems themselves are inherently complex. If information is available, it might as well be used, but what can be done with it? Straightforward, traditional computational solutions to this new problem of complexity can, and usually do, produce very unsatisfactory, unreliable and even unworkable results. Recently however, artificial neural networks, which have been found to be very versatile and powerful when dealing with difficulties such as nonlinearities, multivariate systems and high data content, have shown their strengths in general in dealing with complex problems. This volume brings together a collection of top researchers from around the world, in the field of artificial neural networks.
Table of Contents
Recent Results and Mathematical Methods for Functional Approximation.- Approximation of Smooth Functions.- Incremental Approximation.- Rates of Approximation in a Feedforward Network.- The Use of State Space Control Theory for Analysing Feedforward Neural Networks.- The "Psychological" Limits of Neural Computation.- On the Effectiveness of Memory-Based Methods in Machine Learning.- Neurofuzzy Systems Modelling.- Differential Neurocontrol of Multidimensional Systems.- Geometric Algebra Based Neural Networks.- A Tutorial on the EM Algorithm.- Discrete Event Complex Systems.- Statistical Decision Making.- Feature Selection and Classification by Modified Model.- A Priori Information in Network Design.