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Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model |
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| Authors: | Jing Chen Yan Zhang Ruifeng Ding |
| Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China 2. Wuxi Professional College of Science and Technology, Wuxi, 214028, P.R. China 3. Wuxi Institute of Technology, Wuxi, 214121, P.R. China |
| Abstract: | This paper presents a gradient-based iterative identification algorithm and an auxiliary-model-based multi-innovation generalized extended stochastic gradient algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the gradient-based iterative algorithm are smaller than the generalized extended stochastic gradient algorithm under same data lengths. A simulation example is provided. |
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