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Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
List of contents
Part 1 Understanding and simplifying networks: Analyzing the internal representations of trained neural networks. Information maximization to simplify internal representation. Rule extraction from trained artificial neural networks. Part 2 Novel architectures and algorithms: Pulse-stream techniques and circuits for implementing neural networks. Cellular neural networks. Efficient training of feed-forward neural networks. Exploiting local optima in multiversion neural computing. Part 3 Applications: Neural and neuro-fuzzy control systems. Image compression using neural networks. Oil spill detection: a case study using recurrent artificial neural networks.
Summary
Discusses the main areas of neural networks. This book includes chapters that cover the information from different perspectives. It is suitable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
