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Design and Application of Interval Type-2 TSK Fuzzy Logic System Based on QPSO Algorithm

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Abstract

Studies on interval type-2 TSK fuzzy logic system is a hot topic in the current academic area. Parameter identification is very important for system design. Commonly used parameter identification methods are the least-square algorithm, BP algorithm, etc., few scholars use QPSO algorithm for parameter identification. In this paper, we propose a design of interval type-2 TSK fuzzy logic system based on quantum behaved particle swarm optimization (QPSO) intelligent algorithm. Firstly, by combining the A1–C1, A2–C0, A2–C1 interval type-2 TSK fuzzy logic system with neural network, the fuzzy neural network system is designed. Then, the QPSO intelligent algorithm was used to tune the fuzzy neural network system parameters, and the designed system model is applied to predict the Nasdaq Composite Index and International Gold Prices. Both QPSO algorithm and BP algorithm have been used to train the system model. By comparing QPSO algorithm and BP algorithm, the performance index and simulation results illustrated the proposed model is effective and feasible, which can achieve a better performance. Finally, compare the performance of the four fuzzy logic systems, it can be seen the effect of A2–C1 fuzzy logic system is better than that of the other three fuzzy logic systems.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61374113), and by Liaoning Province College Basic Scientific Research Business Funding Project (JL201615410).

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, China

    Qiu-feng Fan, Tao Wang, Yang Chen & Zhi-feng Zhang

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  1. Qiu-feng Fan
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  2. Tao Wang
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  4. Zhi-feng Zhang
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Correspondence to Tao Wang.

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Fan, Qf., Wang, T., Chen, Y. et al. Design and Application of Interval Type-2 TSK Fuzzy Logic System Based on QPSO Algorithm. Int. J. Fuzzy Syst. 20, 835–846 (2018). https://doi.org/10.1007/s40815-017-0357-3

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