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Sliding mode-based adaptive learning in dynamical adalines

Abstract:

A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.