The iterative identification method for Hammerstein nonlinear channel
Rui Su, Bin Wang, Shigang Liu · 2014
This paper is concerned with the iteration identification algorithm for Hammerstein model with complex-valued input for the fact that the existing algorithms are not valid for complex input. Based on the stochastic gradient algorithm, the extended stochastic gradient algorithm is proposed by defining new cost function for complex input. The extended hierarchical multi-innovation stochastic gradient algorithm is proposed by introducing multi-innovation identification theory and hierarchical principle to the extended stochastic gradient algorithm. Experimental simulations show that the extended hierarchical multi-innovation stochastic gradient algorithm has better performance than the extended stochastic gradient algorithm at the expense of computational complexity.