Identification of Disappeared Volterra Kernels of M-sequence Correlation Method for Nonlinear System

2006 ◽  
Author(s):  
Eiji Nishiyama ◽  
Chisako Nishijima ◽  
Hiroshi Harada ◽  
Hiroshi Kashiwagi
Keyword(s):  
Nonlinear System ◽  
Correlation Method ◽  
Volterra Kernels ◽  
Sequence Correlation ◽  
M Sequence

scholarly journals Estimation of Parameters of Nonlinear System by Using M-sequence and Volterra Kernels

2008 ◽  
Vol 74 (740) ◽  
pp. 766-772
Author(s):  
Hiroshi HARADA ◽  
Yukio TOYOZAWA ◽  
Masahiko SHIGAKI ◽  
Hiroshi KASHIWAGI ◽  
Teruo YAMAGUCHI
Keyword(s):  
Nonlinear System ◽  
Estimation Of Parameters ◽  
Volterra Kernels ◽  
M Sequence

scholarly journals Identifying Odd/Even-Order Binary Kernel Slices for a Nonlinear System Using Inverse Repeat m-Sequences

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jin-yan Hu ◽  
Gang Yan ◽  
Tao Wang
Keyword(s):  
Nonlinear System ◽  
Cross Correlation ◽  
Correlation Method ◽  
Kernel Functions ◽  
Third Order ◽  
Mathematical Properties ◽  
M Sequence ◽  
System Output ◽  
Even Order ◽  
Order Kernel

The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method.

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