2015
DOI: 10.1016/j.apm.2015.01.018
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Identification of nonlinear cascade systems with output hysteresis based on the key term separation principle

Abstract: a b s t r a c tAn approach to modeling and identification of nonlinear cascade systems with a linear dynamic system and an output hysteresis is presented. The proposed mathematical model is based on the application of the key term separation principle and a special form of Coleman-Hodgdon hysteresis model. A least-squares based iterative algorithm with internal variable estimation is used for the cascade systems parameter identification. The feasibility of proposed approach is demonstrated on illustrative exam… Show more

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Cited by 36 publications
(13 citation statements)
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“…Many gradient-based algorithms, including the stochastic gradient algorithms [32][33][34] and the gradient-based iterative algorithms, have been developed using the multi-innovation identification theory, the maximum likelihood estimation methods [35,36], the key-term separation principle [37,38], and the data filtering theory.…”
Section: Introductionmentioning
confidence: 99%
“…Many gradient-based algorithms, including the stochastic gradient algorithms [32][33][34] and the gradient-based iterative algorithms, have been developed using the multi-innovation identification theory, the maximum likelihood estimation methods [35,36], the key-term separation principle [37,38], and the data filtering theory.…”
Section: Introductionmentioning
confidence: 99%
“…The output equation of the Hammerstein system can be constructed from (1) and (2). However, a direct substitution of (2) into (1) would lead to a very complex expression, and the key-term separation principle can be applied [29]. We can rewritten (1) as…”
Section: The System Descriptionmentioning
confidence: 99%
“…In this section, the key term separation principle is employed to parameterize the IN-EEMA systems. The core idea of the key term separation technique is to express the system output as a linear combination of the system parameters [38,39]. Therefore, the redundant parameter estimation can be avoided.…”
Section: The Key Term Separation-based Extended Newton Iterative Algomentioning
confidence: 99%