Forecasting by Interval Type-2 Fuzzy Logic System Optimized with QPSO Algorithm

doi:10.12068/j.issn.1005-3026.2016.10.003

Abstract

Abstract: A kind of interval type-2 fuzzy logic system was designed to investigate forecasting problems based on the historical data. In the process of designing the interval type-2 fuzzy logic system， the antecedent， consequent and input measurement primary membership functions of interval type-2 fuzzy sets were all Gaussian type-2 membership functions with uncertain standard deviation. The quantum particle swarm optimization algorithm was used to tune the parameters of the designed interval type-2 fuzzy logic system. Part of the load competition data of European network on intelligent technologies and the price data of West Texas Intermediate crude oil were used to test the proposed fuzzy logic system forecasting method. Comprehensive evaluation error sum was defined as the forecasting performance index of fuzzy logic system. Simulation studies showed that the proposed interval type-2 fuzzy logic system forecasting methods outperform their corresponding type-1 fuzzy logic system on convergence and stability.

Key words: interval type-2 fuzzy logic system, interval type-2 fuzzy set, quantum particle swarm optimization algorithm, simulation, convergence

CLC Number:

TP182
TP11

CHEN Yang， WANG Da-zhi， NING Wu. Forecasting by Interval Type-2 Fuzzy Logic System Optimized with QPSO Algorithm[J]. Journal of Northeastern University Natural Science, 2016, 37(10): 1379-1383.

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