Stochastic gradient identification of Wiener system with maximum mutual information criterion

This study presents an information-theoretic approach for adaptive identification of an unknown Wiener system. A two-criterion identification scheme is proposed, in which the adaptive system comprises a linear finite-impulse response filter trained by maximum mutual information (MaxMI) criterion and a polynomial non-linearity learned by traditional mean square error criterion. The authors show that under certain conditions, the optimum solution matches the true system exactly. Further, the authors develop a stochastic gradient-based algorithm, that is, stochastic mutual information gradient-normalised least mean square algorithm, to implement the proposed identification scheme. Monte-Carlo simulation results demonstrate the noticeable performance improvement of this new algorithm in comparison with some other algorithms.