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By Vasantha Kalyani David

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Extra resources for Pattern Recognition using Neural and Functional Networks

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This condition is checked using a vigilance parameter. The ART network gets its name from the particular way in which learning and recall interplay in the network. In physics, resonance occurs when a small-amplitude vibration of the proper frequency causes a large-amplitude vibration in an electrical or mechanical system. In ART network, information in the form of processing element outputs, reverberates back and forth between layers. If the proper pattern develops, a stable oscillation ensues, which is the neural network equivalent of resonance.

David and S. Rajasekaran: Pattern Recog. Using Neural & Funct. , SCI 160, pp. 27–49. 1 ct ed Grossberg [14], [18] and his associates have worked for nearly 20 years on theories to explain human cognition in terms of neural network operations. Their research has culminated in a model called ART. In a competitive learning scheme, there is no guarantee that the clusters formed will be stable unless the learning rate gradually approaches zero with iteration. When this happens, the network loses its plasticity.

The field includes work by Ingrid Daubechies, Y. Meyer, Ronald Coifman and Victor Wickerhauser [25], [26]. Wavelet transforms are capable of providing the time and frequency information simultaneously, thus giving a time-frequency representation of a given signal. The wavelet coefficients that have to be calculated at every possible scale involves a lot of data being generated. The dyadic scales and positions are chosen and they are obtained from the discrete wavelet transform (DWT). The Mallat algorithm [70] yields a fast wavelet transform, which allows wavelet coefficients to emerge quickly.

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