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Performance analysis of a wavelet-based hybrid neurosystem for signal classification

This paper is concerned with the problem of determining performance of a wavelet-based hybrid neurosystem trained to provide efficient feature extraction and signal classification. The hybrid network consists of a parallel array of neurosystems. Each neurosystem is constructed with three single neural networks; two of which are feature extraction networks, and the other is a classification network, are provided with magnitude and location information of the wavelet transform coefficients, respectively, and are trained with self-organizing rules. Their outputs are then presented to the classification network for pattern recognition. Based on the topological maps provided by the feature extraction neural networks, the back-propagation algorithm is used to train the third network for pattern recognition. The combination of wavelet, wavelet transform, and hybrid neural network architecture and advanced training algorithms in the design makes the system unique and provides high classification accuracy. In this paper, system performance is shown to be intrinsically related to basis kernel function used in feature extraction. A method for selecting the optimal basis function and a performance analysis using simulated data under various noise condition are presented and compared against other pattern recognition techniques.