Abstract A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system. As a result, it can circumvent the problem of initialization and convergence of the model parameters discussed in the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Example is used to illustrate the effectiveness of the proposed method.
Identification of MIMO Neuro-fuzzy Hammerstein Model with Noises
2014-01-01
9 pages
Article/Chapter (Book)
Electronic Resource
English
Identification of MIMO Neuro-fuzzy Hammerstein Model with Noises
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