scholarly journals A Method for Identifying Volterra Kernels of Nonlinear System Using Low-degree M-sequence

1998 ◽  
Vol 34 (1) ◽  
pp. 9-12 ◽  
Author(s):  
Hiroshi HARADA ◽  
Eiji NISHIYAMA ◽  
Hiroshi KASHIWAGI
Keyword(s):  
Nonlinear System ◽  
Volterra Kernels ◽  
Low Degree ◽  
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

Third-Order Volterra Kernel Identification Technique in Aerodynamics

2011 ◽  
Vol 52-54 ◽  
pp. 618-623
Author(s):  
Yun Hai Wang ◽  
Jing Long Han ◽  
Wei Zhou
Keyword(s):  
Nonlinear System ◽  
Volterra Series ◽  
Volterra Kernel ◽  
Third Order ◽  
Volterra Kernels ◽  
Weakly Nonlinear ◽  
The Third ◽  
Identification Technique ◽  
Volterra Kernel Identification ◽  
Identification Techniques

In this paper, we will extend a Volterra identification technique of nonlinear systems. In reality there exists a large class of weakly Nonlinear System which can be well defined by the first few kernels of the Volterra series. In general, Engineers believe that identifying high-order Volterra kernels is a big problem and hope for the advent of better identification techniques. However, with the extensive development of the Volterra kernels’ identification technique, the situation may improve. The formulas used to calculate kernels up to the third-order are given.

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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