Interval type-2 fuzzy neural networks with asymmetric MFs based on the twice optimization algorithm for nonlinear system identification

Liu, Jiapu; Zhao, Taoyan; Cao, Jiangtao; Li, Ping

doi:10.1016/j.ins.2023.01.134

Cited by 14 publications

(1 citation statement)

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“…Hassan et al [34] used heuristic optimization methods such as genetic algorithms and artificial bee colonies to optimize the antecedent parameters of interval type 2 fuzzy logic systems, and successfully applied them to predict the exchange rate between the New Zealand dollar and the US dollar. Liu et al [35] proposed a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions, for nonlinear system identification problems, which achieved favorable results. Ren et al [36] proposed an interval type-2 (IT2) wind speed prediction model based on the selection of input variables, which provides more accurate forecasting results.…”

Section: Introduction1mentioning

confidence: 99%

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

Li,

Miao

2024

Chin. J. Electr. Eng.

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For short-term wind power forecasting, an interval A2-C1 type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems(IT2 A2-C1 FLS) method based on extended Kalman filter (EKF) optimization algorithm is proposed, where "A" means antecedent and "C" consequent. Compared with the type-1 FLS model, IT2 TSK FLS method can simultaneously model both intra-individual and inter-individual uncertainty, and further optimize the antecedent and consequent parameters using EKF to further improve the forecasting performance. The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region, under the same conditions, it is also compared with the type-1 TSK FLS and IT2 TSK FLS methods with the back propagation (BP) and particle swarm optimization (PSO) algorithms, as well as IT2 A2-C0 TSK FLS methods with EKF. Experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to other FLS methods in performance,showing its effectiveness and application potential.

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Section: Introduction1mentioning

confidence: 99%