Description: Neural networks have found a range of applications in recent years, providing a powerful approach to solving problems in such areas as machine learning and pattern recognition. The underlying mathematics, however, is different to the discrete type familiar to computer scientists and specialists in Artificial Intelligence. In fact, it has much more in common with classical applied mathematics, especially in such fields as dynamical systems, control theory, numerical analysis and approximation theory, as well as various branches of statistics. This book introduces the deterministic aspects of the mathematical theory behind neural networks in a comprehensive way. It should appeal to both mathematically-oriented neural network specialists and to classical applied mathematicians interested in neural networks.

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