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Reseach Article

Fuzzy Approach to Regulate S-type Biological Systems

by Shinq-Jen Wu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 10
Year of Publication: 2021
Authors: Shinq-Jen Wu
10.5120/ijca2021921409

Shinq-Jen Wu . Fuzzy Approach to Regulate S-type Biological Systems. International Journal of Computer Applications. 183, 10 ( Jun 2021), 50-55. DOI=10.5120/ijca2021921409

@article{ 10.5120/ijca2021921409,
author = { Shinq-Jen Wu },
title = { Fuzzy Approach to Regulate S-type Biological Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 10 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number10/31967-2021921409/ },
doi = { 10.5120/ijca2021921409 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:28.463507+05:30
%A Shinq-Jen Wu
%T Fuzzy Approach to Regulate S-type Biological Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 10
%P 50-55
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is important to regulate biological systems to return to nominal steady states such that systems are able to maintain normal functions.S-type biological systems(S-systems) are described as power-law-based differential equations which are able to shownetinteractive strength between constitutes and a result, S-system becomes the most potential model for large-scale systems. Biological systems always possess a lot of uncertainties and noises. Fuzzy sets and models are able to describe, recognize and manipulate data that are vague and lack certainty.However, biological systems are different from electromechanical systems thatallow various types of time-varying signalsas system inputs. Therefore, step functions are used as fuzzy outputs to denote constant concentration and the firing strength of each fuzzy rule is the blending or allocating ratio.A cascade pathway is concerned andan exponentially decaying model is used to describe the functional degradation phenomenon. Dry-lab experiments are carried out in five different situations. Simulation results show that the proposed seven-rule fuzzy logic controllers are able to find out the nominal values of independent variables and force systems to return to their nominal steady states.The larger the nominal values are the longer the time to reach targets.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy logic control systems biology computational biology