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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 34, Number 1(2) (2022)
Copyright(C) MYU K.K.
pp. 105-120
S&M2797 Research Paper of Special Issue
https://doi.org/10.18494/SAM3549
Published: January 27, 2022

Neural Fuzzy Inference Network Based on Differential Evolution for Nonlinear System Control [PDF]

Hsueh-Yi Lin and Cheng-Jian Lin

(Received April 28, 2021; Accepted November 12, 2021)

Keywords: temperature-sensing control, differential evolution, symbiotic evolution, neural fuzzy networks, Taguchi method

We propose a neural fuzzy inference network (NFIN) based on a symbiotic Taguchi-based modified differential evolution (STMDE) algorithm for solving nonlinear control problems. The proposed STMDE algorithm not only uses the Taguchi method in its search for the best individual but also employs adjustable parameter control to tune the scaling factor, which can prevent a solution from being trapped at local optima and reinforce the search ability. Moreover, symbiotic evolution (SE) is applied to improve the structure of individual compositions. Unlike the traditional differential evolution (DE) algorithm, SE regards each individual in a population as being the partial solution to a problem instead of the full solution. Compared with traditional DE, the proposed STMDE algorithm reduces the error by 7.95, 4.51, 5.22, and 51.34% in terms of regulation performance, noise rejection ability, robustness to parameter variation of the controlled system, and controller tracking capability, respectively. In addition, our experimental results also indicate that the proposed STMDE algorithm exhibits superior performance to other algorithms used for solving nonlinear temperature-sensing control problems.

Corresponding author: Cheng-Jian Lin


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Cite this article
Hsueh-Yi Lin and Cheng-Jian Lin, Neural Fuzzy Inference Network Based on Differential Evolution for Nonlinear System Control, Sens. Mater., Vol. 34, No. 1, 2022, p. 105-120.



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