Handbook of Neural Network Signal Processing

Citation

Rigelsford, J. (2003), "Handbook of Neural Network Signal Processing", Industrial Robot, Vol. 30 No. 4. https://doi.org/10.1108/ir.2003.04930dae.002

Handbook of Neural Network Signal Processing

Handbook of Neural Network Signal Processing

Yu Hen Hu and Jenq-Neng Hwang (Eds)CRC Press2002600 pp.ISBN 0-8493-2359-2£93.00

Keywords: Neural networks, Signal processing

The "Handbook of Neural Network Signal Processing" is a clearly written and comprehensive reference text, which provides a practical examination of neural networks and their applications.

The book comprises 13 chapters addressing three focal areas. The first five chapters provide in-depth surveys of recent progress in neural network computing algorithms. An Introduction to Neural Networks for Signal Processing is given in chapter 1, while chapter 2 discusses Signal Processing Using the Multi-layer Perceptron. Radial Basis Functions, and An Introduction to Kernal-Based Learning Algorithms, are discussed in chapters 3 and 4, respectively. Chapter 5, Committee Machines, presents topics including average, bagging, and boosting; the mixture of experts and its variants; and a Bayesian committee machine.

The following four chapters address neural network implementations of important signal processing problems. Chapter 6, Dynamic Neural Networks and Optimal Signal Processing, discuss function approximation and adaptive systems, topological approximations with static nonlinear combinations of linear finite memory operators, and the construction of linear approximately finite memory operations. Chapter 7 presents Blind Separation and Blind Deconvolution, while chapter 8 addresses Neural Networks and Principal Component Analysis. Applications of Artificial Neural Networks to Time Series Prediction are given in chapter 9.

The remaining four chapters of the book examine signal processing applications and systems that use neural network methods. Fundamentals of speech recognition, the generalised probabilistic descent method, recurrent networks, and signal separation, are amongst the topics discussed in chapter 10, Applications of Artificial Neural Networks (ANNs) to Speech Processing. Learning and Adaptive Characterisation of Visual Contents in Image Retrieval Systems are presented in chapter 11, while Applications of Neural Networks to Biomedical Image Processing are discussed in chapter 12. The final chapter of the book addresses Hierarchical Fuzzy Neural Networks for Pattern Classification.

The "Handbook of Neural Network Signal Processing" provides some of the latest developments in neural networks and their applications. IT is suitable for undergraduate and graduate students who are already familiar with neural networks and will be invaluable to researchers and practitioners who are using neural networks for statistical signal processing.