Functional networks with applications: a neural-based ... - IEEE Xplore

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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 4, JULY 1999

Books in Brief Functional Networks with Applications: A Neural-Based Paradigm— E. Castillo, A. Cobo, J. M. Gutierrez, and R. E. Pruneda. (Boston, MA: Kluwer, 1999, 320 pp., hard cover, $132.00. ISBN 0-7923-8332-X). This book introduces “functional networks,” a novel neural-based paradigm, and shows that functional network architectures can be efficiently applied to solve many interesting practical problems. Functional networks allow for a more general class of units than the sigmoidal units used in neural networks. They can reproduce some physical or engineering properties; the initial functional network can arise directly from the problem under consideration. Estimation of functional net parameters can, in many cases, be obtained by solving a linear system of equations. This means a quick and unique solution: the global minimum. The book is addressed to a wide audience including computer scientists, engineers, mathematicians, etc. No strong prerequisites are assumed, though a previous knowledge of neural networks is convenient. The book consists of 11 chapters grouped in four parts: Neural Networks, Functional Networks, Applications, and Computer Programs. The book also includes an Index. The contents are as follows: “Introduction to Neural Networks,” “Introduction to Functional Networks,” “Functional Equations,” “Some Functional Network Mod-

Publisher Item Identifier S 1045-9227(99)06393-6.

els,” “Model Selection,” “Applications to Time Series,” “Applications to Differential Equations,” “Application to CAD,” “Appliocations to Regression,” “Mathematica Programs,” and “A Java Applet.” Independent Component Analysis: Theory and Applications— Te-Won Lee. (Boston, MA: Kluwer, 1998, 237 pp., hard cover. ISBN 0-7923-8261-7). Independent component analysis (ICA) is a signal processing method to extract independent source given only observed data that are mixtures of the unknown sources. This book presents theories and applications of ICA and includes examples of several real-world applications. Based on theories in probabilistic models, information theory, and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as maximum likelihood estimation, negentropy maximization, and infomax are reviewed and put in an information theoretic framework to unify several lines of ICA research. The book consists of ten chapters. The contents are as follows: “Basics,” “Independent Component Analysis,” “A Unifying Information-Theoretic Framework for ICA,” “Blind Separation of Time-Delayed and Convolved Sources,” “ICA Using Overcomplete Representations,” “First Steps Towards Nonlinear ICA,” “Biomedical Applications of ICA,” “ICA for Feature Extraction,” “Unsupervised Classification with ICA Mixture Models,” and “Conclusions and Future Research.”

1045–9227/99$10.00  1999 IEEE