Neural networks are one of the fast-growing paradigms for learning
systems with a wide variety of potential applications in industry.
In particular there are general results which prove the universal
applicability of neural networks to many problems. There is also an
ever greater understanding of the underlying manner in which tasks
such as classification can be solved optimally by this host of
techniques. Through the application of ideas of statistics,
dynamical systems theory and information theory the methods are
likely to become ever more effective for solving problems
previously found to be difficult to tackle using standard
techniques. This book compares and contrasts the academic theory
and the industrial reality, with case studies and latest research
findings from international experts. The contributions describe
application areas including finance, digital data transmission,
hybrid systems, automotive and aerospace industries, pattern
analysis in clinical psychiatry, time series prediction, and
genetic and neural algorithms. This book demonstrates the vigour
and strength of the subject in solving hard problems and as such
will be of great interest to all researchers and professionals with
an interest in neural networks.
General
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