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Parameters optimization and precision enhancement of Takagi–Sugeno fuzzy neural network

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Abstract

Takagi–Sugeno fuzzy neural network (TSFNN) has been widely used in intelligent prediction. The prediction accuracy of TSFNN is impacted by its model parameter choices. However, the manual selection of parameters is hard to ensure the prediction accuracy of TSFNN. Therefore, we propose a Particle Swarm Optimization with Result Precision Enhancement strategy, PSO-RPE, to automatically generate highly optimized parameters to improve the prediction accuracy of TSFNN. In PSO-RPE, we present memory enhancement technology to direct the evolution of particles and accelerate the search process for the solution. Next, our PSO-RPE strategy employs a customized linear dynamic equation to balance the global and local search capabilities. Moreover, the elite strategy is applied to produce better feasible parameters with high probability. Moreover, the analysis of the convergence on 12 benchmarks is given to verify the superiority of PSO-RPE. Finally, our experiments in predicting water quality based on TSFNN demonstrate that the PSO-RPE strategy is effective in the generation of optimized parameters.

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Enquiries about data availability should be directed to the authors.

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 62272069, 62203077, 62203077, 62202072), Natural Science Key Foundation of Chongqing (cstc2020jcyj-zdxmX0026), Fundamental Research Funds for the Central Universities (2021CDJXXXB006), Zhejiang Lab (No. 2021LC0AB01), and Natural Science Foundation of Chongqing under Grant No. CSTB2022NSCQ-MSX1029.

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  1. Songtao Guo

    Present address: College of Computer Science, Chongqing University, Chongqing, 400044, People’s Republic of China

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, People’s Republic of China

    Dewen Qiao, Pengzhan Zhou & Mingyan Li

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  1. Dewen Qiao
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  2. Pengzhan Zhou
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Contributions

DQ PZ, and ML are responsible for the conception and writing of this manuscript, and SG oversees the overall structure of the manuscript.

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Correspondence to Songtao Guo.

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Dewen Qiao, Pengzhan Zhou, Mingyan Li, and Songtao Guo declare that they have no conflict of interest.

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Qiao, D., Zhou, P., Li, M. et al. Parameters optimization and precision enhancement of Takagi–Sugeno fuzzy neural network. Soft Comput (2024). https://doi.org/10.1007/s00500-024-09743-7

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