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Fuzzy Approach to Regulate S-type Biological Systems

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Shinq-Jen Wu

Shinq-Jen Wu. Fuzzy Approach to Regulate S-type Biological Systems. International Journal of Computer Applications 183(10):50-55, June 2021. BibTeX

	author = {Shinq-Jen Wu},
	title = {Fuzzy Approach to Regulate S-type Biological Systems},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {10},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {50-55},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921409},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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Fuzzy logic control, systems biology, computational biology