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Bilinear model parameter identification method based on decomposition technique

A technology of decomposition technology and identification method, which is applied in the field of bilinear model parameter identification based on decomposition technology, and can solve problems such as over-parameterization

Inactive Publication Date: 2018-02-23
JIANGNAN UNIV
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Problems solved by technology

However, the parameterized identification model contains cross terms between linear modules and nonlinear modules, resulting in the need to identify far more parameters to be identified than the original parameters of the nonlinear model
Some identification methods such as least squares, two-stage, and Kalman filtering have also been used to identify model parameters, but there are problems with over-parameters

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  • Bilinear model parameter identification method based on decomposition technique
  • Bilinear model parameter identification method based on decomposition technique
  • Bilinear model parameter identification method based on decomposition technique

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Embodiment Construction

[0023] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0024] The specific steps are:

[0025] 1: The input signal sequence u(t) and the output signal sequence y(t) of the acquisition object, where t=0, 1, 2, . . .

[0026] 2: Combining the input signal and the nonlinear function f(·) of the known basis to obtain the relationship expression between the output and input of the nonlinear module in is a row vector composed of basis functions, is a column vector of the nonlinear block parameters to be identified.

[0027] 3: The output of the nonlinear module Through the linear module G(z)=a 1 z- 1 +a 2 z- 2 +…+a m z- m After that, the output of the input nonlinear finite impulse response model can be obtained, further considering the interference of the model, and finally the expression of the bilinear model can be obtained as:

[0028]

[0029] where F(t):=[f(u(t)),f(u(t-1)),...,...

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Abstract

The invention discloses a bilinear model parameter identification method based on a decomposition technique, comprising: reconstructing a bilinear model into two equivalent virtual models, defining acriterion function for a parameter vector of each virtual model, minimizing the criterion functions by means of the negative gradient search principle to obtain a stochastic estimation algorithm for the two parameter vectors. Previous-moment estimated values of unknown parameters are used to replace current-moment values, so that the problem can be effectively solved that an estimation algorithm cannot be implemented since information vectors of the two virtual models contain unknown variables; a forgetting factor introduced to the algorithm is effective to improve converging speed of the algorithm; the forgetting factor hierarchical stochastic gradient identification algorithm finally obtained is capable of quickly and effectively identifying parameters of a bilinear model.

Description

technical field [0001] The invention relates to the technical field of parameter identification, in particular to a bilinear model parameter identification method based on decomposition technology. Background technique [0002] The block-structured nonlinear model is a very important kind of nonlinear model, which has been widely concerned and deeply studied in academia and industry. If the output of the block-structured nonlinear module can be expressed as a linear combination of known basis functions, the nonlinear model can be converted into a bilinear model. The most direct and commonly used method for parameter identification of the bilinear model is through The parametric method, by reconstructing the parameters of the nonlinear model, makes the output look linear in the unknown parameter space, so that the identification methods suitable for linear models can be used to identify its parameters. However, the parameterized identification model contains cross terms betw...

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Application Information

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IPC IPC(8): G06F17/16G06K9/00
CPCG06F17/16G06F2218/12
Inventor 肖永松丁锋汪学海刘艳君
Owner JIANGNAN UNIV
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