Connectionist techniques for signal processing

Neural network or connectionist techniques are currently generating considerable excitement in areas as diverse as financial analysis and cognitive psychology. The author presents an introduction to neural networks and their possible application to signal processing tasks. A neural network is an interconnected structure of many simple nonlinear processing elements or neurons, which learn from examples to form an internal representation of a problem. These processors are often analogue in operation, and by the standards of modern digital circuitry may be very slow. Layers within a network represent degrees of abstraction, and very complicated representations can be constructed.<
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