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Intelligent Signal Estimation Using Cosine Neural Networks with Variable Structure Systems Based Training Procedure

This paper demonstrates the estimation of signals by using a neural network structure composed of cosine neurons. The building blocks of the architecture are cosine components with adjustable amplitude, frequency and phase. The training procedure is based on the mixture of gradient descent with a method utilizing sliding mode control philosophy. The proposed use of mixed information in training dynamics leads to the minimization of the cost of estimation as well as the cost of stability. Two application examples are presented in the paper. The first example considers the reconstruction of a time signal having finite frequency components in the spectrum. The second example shows the reconstruction of the frequency plot of a FIR filter.