Several Stochastic Gradient Algorithms for Nonlinear Systems with Hard Nonlinearities

This paper studies several identification methods for Hammerstein systems with piece-wise linearities. By using the key term separation technique, the model of the nonlinear Hammerstein systems be changed to an identification model, then based on the derived model, a stochastic gradient identification algorithm, a forgetting factor stochastic gradient algorithm and a modified stochastic gradient algorithm are used to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithms.