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Pattern Recognition Using Neural and Functional Networks (Studies in Computational Intelligence)

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Biologically inspired computing is different from conventional computing. It has a different feel; often the terminology does not sound like it’s talking about machines. The activities of this computing sound more human than mechanistic as people speak of machines that behave, react, self-organize, learn, generalize, remember and even to forget. Much of this technology tries to mimic nature’s approach in order to mimic some of nature’s capabilities. They have a rigorous, mathematical basis and neural networks for example have a statistically valid set on which the network is trained.

Two outlines are suggested as the possible tracks for pattern recognition. They are neural networks and functional networks. Neural Networks (many interconnected elements operating in parallel) carry out tasks that are not only beyond the scope of conventional processing but also cannot be understood in the same terms. Imaging applications for neural networks seem to be a natural fit. Neural networks love to do pattern recognition. A new approach to pattern recognition using microARTMAP together with wavelet transforms in the context of hand written characters, gestures and signatures have been dealt. The Kohonen Network, Back Propagation Networks and Competitive Hopfield Neural Network have been considered for various applications.
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