Design of an Interval Type-۲ TSK Fuzzy System for System Identification with Application to Time-Series Prediction
Publish place: Fourth International Conference on Soft Computing
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG04_038
تاریخ نمایه سازی: 23 اسفند 1400
Abstract:
Type-۲ fuzzy systems have three-dimensional membership functions and a footprint of uncertainties. Hence, they could handle higher levels of uncertainties in comparison with type-۱ fuzzy systems. Here, we employ interval type-۲ Takagi-Sugeno-Kang (TSK) fuzzy systems for system identification and time-series prediction. The designed type-۲ fuzzy system uses direct defuzzification which avoids the computationally extensive calculations of the Karnik-Mendel algorithm. A type-۱ fuzzy system is also designed in the same way for comparative purposes. The weights of the fuzzy systems are updated using the gradient-descent algorithm. The performance of the two fuzzy systems is evaluated in chaotic time-series prediction. Simulation results show that the type-۲ fuzzy system reaches lower error for different levels of measurement noise
Keywords:
Interval type-۲ fuzzy systems , direct defuzzification , gradient descent algorithm , Takagi-Sugeno-Kang fuzzy systems , time series prediction .
Authors
F Baghbani
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
Mehraneh Hemmati
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran