Artificial Neural Networks
Written with the beginner in mind, this volume offers an exceptionally clear and thorough introduction to neural network at an elementary level. It provides the clear survey of basic neural network architectures and learning rules. In it, We emphasize mathematical analysis of networks, methods for training network, and application of neural network to practical engineering problems in speech recognition, stock market prediction and pattern recognition.
- Fundamentals of Neural Networks
- Perceptrons
- Backpropagation
- Adaline and Madaline
- Supervised and Unsupervised Learning
- Counter-propagation Network
- Adaptive Resonance Theory
- Neocognitron
- Bidirectional Associative Memory
- Case Studies
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