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Construct and Visualize Three Fuzzy Controllers for Biological S-Systems

by Shinq-Jen Wu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 3
Year of Publication: 2023
Authors: Shinq-Jen Wu
10.5120/ijca2023922697

Shinq-Jen Wu . Construct and Visualize Three Fuzzy Controllers for Biological S-Systems. International Journal of Computer Applications. 185, 3 ( Apr 2023), 53-60. DOI=10.5120/ijca2023922697

@article{ 10.5120/ijca2023922697,
author = { Shinq-Jen Wu },
title = { Construct and Visualize Three Fuzzy Controllers for Biological S-Systems },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 3 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 53-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number3/32688-2023922697/ },
doi = { 10.5120/ijca2023922697 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:53.973265+05:30
%A Shinq-Jen Wu
%T Construct and Visualize Three Fuzzy Controllers for Biological S-Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 3
%P 53-60
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biological S-systems use power-law-based differential equations to show net interactive strength between constitutes. In medium-sized or large-scale systems, dynamic constants of S-systems represent the net value of the action strength, rather than the actual strength. As a result, S-systems becomes the most potential model for large-scale systems. Moreover, biological systems are always subject to uncertainty and noise. Fuzzy logic controllers are developed for handing uncertainty, imprecision, and complexity in the real world. Noise, uncertainty, and the interactive information are all implied in fuzzy if-then rules. In this study, the previously proposed optimal fuzzy controller is used to smoothly regulate biological systems to target states with minimum input consumption. Then, an integrated fuzzy proportional-integral-derivative controller (integrated fuzzy PID) and a pole-placement-based fuzzy controller for biological S-systems are proposed. Additionally, these fuzzy controlled systems are all visualized in block diagrams to provide biological researchers a friendly environment. For these three kinds of fuzzy controllers, only three control rules are used to control a cascade biological system. Simulation results shows that nearly perfect results are achieved for all these three controllers.

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Index Terms

Computer Science
Information Sciences

Keywords

T-S Fuzzy systems pole-placement optimal fuzzy control

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