An Immune-Inspired, Dependence-Based Approach to Blind Inversion of Wiener Systems

In this work, we present a comparative analysis of two methods — based on the autocorrelation and autocorrentropy functions — for representing the time structure of a given signal in the context of the unsupervised inversion of Wiener systems by Hammerstein systems. Linear stages with and without feedback are considered and an immune-inspired algorithm is used to allow parameter optimization without the need for manipulating the cost function, and also with a significant probability of global convergence. The results indicate that both functions provide effective means for system inversion and also illustrate the effect of linear feedback on the overall system performance.

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