Artificial Neural Networks

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Book Summary:

Designed as an introductory level textbook on Artificial Neural Networks at the postgraduate and senior undergraduate levels in any branch of engineering, this self-contained and well-organized book highlights the need for new models of computing based on the fundamental principles of neural networks.

Professor Yegnanarayana compresses, into the covers of a single volume, his several years of rich experience, in teaching and research in the areas of speech processing, image processing, artificial intelligence and neural networks. He gives a masterly analysis of such topics as Basics of artificial neural networks, Functional units of artificial neural networks for pattern recognition tasks, Feedforward and Feedback neural networks, and Archi-tectures for complex pattern recognition tasks. Throughout, the emphasis is on the pattern processing feature of the neural networks. Besides, the presentation of real-world applications provides a practical thrust to the discussion.

Key Features:

The fairly large number of diagrams, the detailed Bibliography, and the provision of Review Questions and Problems at the end of each chapter should prove to be of considerable assistance to the reader. Besides students, practising engineers and research scientists would cherish this book which treats the emerging and exciting area of artificial neural networks in a rigorous yet lucid fashion.

Table of Contents:

Preface. Acknowledgements. Introduction. Basics of Artificial Neural Networks. Activation and Synaptic Dynamics. Functional Units of ANN for Pattern Recognition Tasks. Feedforward Neural Networks. Feedback Neural Networks. Competitive Learning Neural Networks. Architectures for Complex Pattern Recognition Tasks. Applications of ANN. Appendices. Bibliography. Author Index. Subject Index.