Volterra-Wiener Characterization

Volterra-Wiener characterization of non-linear dynamic systems
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Updated 9 Jun 2019

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Volterra-Wiener characterization of non-linear dynamic systems subjected to Gaussian white noise input signal.

function [h0,h1,h2]=Volterra_Wiener_Id(x,y,MaxLag1,MaxLag2)

Input
x: input signal of size (N*1)
y: output signal of size (N*1)
MaxLag1: Max number of lags for the first kernel
MaxLag2: Max number of lags for the second kernel

output
h0: zero order kernel
h1: first oder kernel of size(MaxLag1+1,1)
h2: second order kernel of size(MaxLag2+1,MaxLag2+1)
-------------------------------

Identified model can be verified by forecasting ouput from the input with the identified kernels using the following function:

function y=Volterra_Wiener_Forecast(x,h0,h1,h2)

Input
x: input signal of size (N*1)
h0: zero order kernel
h1: first oder kernel of size(MaxLag1+1,1)
h2: second order kernel of size(MaxLag2+1,MaxLag2+1)

output
y: output signal of size (N*1)

Cite As

Ayad Al-Rumaithi (2024). Volterra-Wiener Characterization (https://www.mathworks.com/matlabcentral/fileexchange/71799-volterra-wiener-characterization), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2017b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.8

note about input type added.

1.0.7

files

1.0.6

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1.0.5

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1.0.4

forecasting function added

1.0.3

description

1.0.2

summary

1.0.1

description

1.0.0