Higher-Order-Cumulant Based Memory Effect Identification of Hammerstein Model
基于高阶累积量的Hammerstein模型记忆效应辨识

This paper focused on memory effect identification of Hammerstein model in Uaussian noise. When input statistics and nonlincarity of Hammerstcin arc unknown, by using higher order cumulant of output signal, two sets linear cquations were proposed to extract coefficients of linear block with memory. Theoretical derivation shows that those two sets of linear equations have unique solutions. I}hey could be used alternately to identify the memory effect of Hammerstein model, and the identification process is not affected by memoryless nonlinear block. Finally, simulations verify that the new developments have higher performance than direct extraction method.