On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases.