Two Identification Methods for a Nonlinear Membership Function

Yuejiang Ji, Lixin Lv and Quanmin Zhu

Complexity, 2021, vol. 2021, 1-7

Abstract: This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5515888

DOI: 10.1155/2021/5515888

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