Nonlinear adaptive filtering of systems with hysteresis by quantized mean field annealing

A technique for nonlinear adaptive filtering of systems with hysteresis has been developed which combines quantized mean field annealing (QMFA) and conventional RLS/FTF adaptive filtering. Hysteresis is modeled as a nonlinear system with memory. Unlike other methods which rely on Volterra and Wiener models, this technique can efficiently handle large order nonlinearities with or without hysteresis effects. The nonlinear channel is divided into a memory nonlinearity followed by a dispersive linear system. Assuming that the dispersive linear system is stationary during initialization, and the nonlinearity does not change while the dispersive linear system varies with time, QMFA is applied to obtain the coefficients and the order of the memory of the nonlinearity and RLS/FTF is applied to determine the weights of the dispersive linear system. Application of this method to a full duplex digital subscriber loop is made. Simulations show the superior performance of our technique compared to that of ordinary RLS/FTF and steepest-descent algorithms