S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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

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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