Two Identification Methods for a Nonlinear Membership Function
Yuejiang Ji, Lixin Lv · Complexity · 2021
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.