My Commercially
Published Books
Neural Networks for Intelligent Signal
Processing
Anthony Zaknich
World Scientific Publishing,
Series on Advanced Biology and Logic-Based Intelligence, Vol. 4, 2003, ISBN
981-238-305-0
This
book provides a thorough theoretical and practical introduction to the
principles and application of a useful set of both supervised and unsupervised
learning neural networks to pattern recognition and intelligent signal
processing. It has been tested on students, unfamiliar with neural networks,
who were able to pick up enough details to successfully complete their PhD,
Masters or final year undergraduate projects. There are many practical
hints, design rules, and examples provided to aid understanding
and application. Original research and a comprehensive description
is given on a class of neural networks termed common bandwidth
spherical basis function neural networks, including the Probabilistic Neural
Network (PNN), the Modified Probabilistic Neural Network (MPNN) and
the General Regression Neural Network (GRNN). An introduction to Vladimir
Vapnik's Statistical Learning Theory (SLT) and related Support Vector Machine
(SVM) adds an extra dimension to the book. SLT provides a suitable
theoretical basis to a number of neural networks including the Multi-Layer
Perceptron (MLP) and Radial Basis Function Neural Networks (RBFNN) as well
as other learning machines. Many of the ideas and discussions throughout the
book may provide valuable alternative views of Intelligent Signal
Processing (ISP) principles and issues.
Also available from World
Scientific Errata: Neural Networks
for Intelligent Signal Processing Principles of Adaptive Filters and
Self-learning Systems
Anthony
Zaknich Springer-Verlag, Series on Advanced Textbooks in
Control and Signal Processing, 2005, ISBN 1-85233-984-5. This
book can be used as a textbook for a one semester undergraduate or postgraduate
introductory course on adaptive and self-learning systems for signal processing
applications. Important topics are introduced and discussed sufficiently to
give the reader adequate background to be able to confidently pursue them at
depth in more advanced literature. Tutorial problems and exercises identify the
significant points and are designed to demonstrate the practical relevance of
the theory. Answers to the tutorials are given to aid topic understanding. To
further facilitate course preparation a typical course outline and sample
examination material is provided. The book’s topics are presented in a
progressive sequence from a short introduction to adaptive filters, linear
systems, and stochastic process theory, to system and signal modelling, the
classical Wiener filter, the Kalman filter, spectral analysis theory, classical
adaptive filters, adaptive control systems and the application of adaptive
filtering, through to nonclassical
adaptive systems. It offers a comprehensive review of linear and stochastic
theory as well as a design guide for the application of the least squares
estimation method and Kalman filters. Although the book concentrates on the
more established adaptive systems theory an introduction to neural networks,
fuzzy logic and genetic algorithms as adaptive systems is also included to
provide a more generic perspective to the topic of adaptive learning. A
significant further offering of the book is a method to seemlessly combine a
set of both classical and/or nonclassical adaptive systems to form a powerful
self-learning engineering solution method. This method is referred to as
sub-space adaptive filtering and is suitable for solving very complex nonlinear
problems typical of the underwater acoustic signal processing and other equally
difficult application domains. Also
available from Amazon.com Errata: Principles of
Adaptive Filters and Self-learning Systems
