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A new hybrid neural architecture (MLP+RPE) for hetero association: multi layer perceptron and coupled recursive processing elements neural networks

This paper addresses a new hybrid neural network, which joins the classical multi layer perceptron (MLP) with a neural network composed of coupled recursive processing elements (RPEs). The individual characteristics of each one of these architectures, once combined, permitted the implementation of input-output mappings where the input patterns can be either discrete or analog and the output patterns can be discrete. Experiments for the performance evaluation of this hybrid neural architecture employing nodes that exhibit bifurcation and chaotic dynamics are described and the results addressing the operation under analog noise added to the input patterns are presented and analyzed. The performance of the MLP+RPE architecture was contrasted with the performance of other hybrid arrangements that have the same functionality (arrangements joining the MLP and Hopfield architectures), and the obtained results indicate that the MLP+RPE architecture presents significant superiority.