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| Identification of the Hammerstein nonlinear system with noisy output measurements |
| QimingZha1,FengLi1,RanranLiu2 |
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| (1 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China;2 School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China) |
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| 摘要: |
| In this research, we present a methodology to identify the Hammerstein nonlinear system with noisy output measurements.
TheHammerstein system presented is comprised of neural fuzzy model (NFM) as its static nonlinear block and auto-regressive
with extra input (ARX) model as its dynamic linear block, and a two-step procedure is accomplished using signal combination.
In the first step, in the case of input–output of Gaussian signals, the correlation function-based least squares (CF-LS) technique
is utilized to identify the linear block, solving the problem that the intermediate variable connecting nonlinear and linear blocks
cannot be measured. In the second step, to improve the identification accuracy of the nonlinear block parameters, an improved
particle swarm optimization technique is developed under input–output of random signals. The validity and accuracy of the
presented scheme are verified by a numerical simulation and a practical nonlinear process, and the results illustrate that the
proposed methodology can identify well the Hammerstein nonlinear system with noisy output measurements. |
| 关键词: Hammerstein nonlinear system · Signal combination · Auto-regressive with extra input · Improved particle swarm optimization |
| DOI：https://doi.org/10.1007/s11768-024-00196-9 |
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| 基金项目:This work was supported by the National Natural Science Foundation of China (62003151), the Changzhou Science and Technology Bureau (CJ20220065, CM20223015), the Qinglan Project of Jiangsu Province of China, and the Zhongwu Youth Innovative Talents Support Program in Jiangsu University of Technology. |
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| Identification of the Hammerstein nonlinear system with noisy output measurements |
| Qiming Zha1,Feng Li1,Ranran Liu2 |
| (1 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China;2 School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China) |
| Abstract: |
| In this research, we present a methodology to identify the Hammerstein nonlinear system with noisy output measurements.
TheHammerstein system presented is comprised of neural fuzzy model (NFM) as its static nonlinear block and auto-regressive
with extra input (ARX) model as its dynamic linear block, and a two-step procedure is accomplished using signal combination.
In the first step, in the case of input–output of Gaussian signals, the correlation function-based least squares (CF-LS) technique
is utilized to identify the linear block, solving the problem that the intermediate variable connecting nonlinear and linear blocks
cannot be measured. In the second step, to improve the identification accuracy of the nonlinear block parameters, an improved
particle swarm optimization technique is developed under input–output of random signals. The validity and accuracy of the
presented scheme are verified by a numerical simulation and a practical nonlinear process, and the results illustrate that the
proposed methodology can identify well the Hammerstein nonlinear system with noisy output measurements. |
| Key words: Hammerstein nonlinear system · Signal combination · Auto-regressive with extra input · Improved particle swarm optimization |
