CFP last date
22 April 2024
Reseach Article

Construct and Visualize Three Fuzzy Controllers for Biological S-Systems

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

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 = { },
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

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.

  1. Tyson, J. J. 1991. Modeling the cell division cycle: cdc2 and cyclin interactions. Proc. Natl. Acad. Sci. USA 88:7328-7332.
  2. Tyson, J. J., Chen, K. C., and Novak, B. 2003. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15:221-231.
  3. Tavassoly, I., Parmar, J., Shajahan-Haq, A,, Clarke, R., Baumann, W., and Tyson, J. J. 2015. Dynamic modeling of the interaction between autophagy and apoptosis in mammalian cells. CPT Pharmacometrics Syst Pharmacol 4(4): 263-72.
  4. Liu, P. K., and Wang, F. S. 2010. Inverse problems of biological systems using multi-objective optimization. Chin. J. Chem. Eng. 39(5):399-406.
  5. Wu, S. J., Wu, C. T., and Chang, J. Y. 2012. Fuzzy-based self-interactive multi-objective evolution optimization for reverse engineering of biological networks. IEEE Trans Fuzzy Syst 20(5):865-882.
  6. Wu, S. J., Wu, C. T., and Chang, J. Y. 2013. Adaptive neural-based fuzzy modeling for biological systems. Math Biosci 242(2): 153-60.
  7. Wu, S. J., and Wu, C. T. 2013. Computational optimization for S-type biological systems: cockroach genetic algorithm. Math Biosci 245(2):299-313.
  8. Wu, S. J, and Wu, C. T. 2014. Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems. Comput Biol Chem 53(2):292-307.
  9. Wu, S. J, and Wu, C. T. 2015. A bio-inspired optimization for inferring interactive networks: cockroach swarm evolution. Expert Syst Appl 42(6): 3253-3267.
  10. Wu, S. J, and Wu, C. T. 2018. Smarten up computational intelligence to decipher time series data. Appl Soft Comput 72:442-456.
  11. Liu, S., Tao, C., Huang, Z, and Huang S. 2010. Modeling of p53 signaling pathway based on S-system equations. Journal of Biomedical Engineering 27(3):505-10 (Chinese)
  12. Luo, Z. P., An, K. N. 2001. Fuzzy systems in biomedical science. Int. J. Gen. Syst. 30(2):209–217.
  13. Komiyama, M., Yoshimoto, K., Sisido, M., Ariga, K. 2017. Chemistry can make strict and fuzzy controls for bio-Systems: DNA nanoarchitectonics and cell-macromolecular nanoarchitectonics. Bull. Chem. Soc. Jpn. 90(9):967–1004.
  14. Abyad, M., Karama, A., and Khallouq, A. 2017. Modelling and control of a biological process using the fuzzy logic Takagi-Sugeno. In Proceeding of the 2017 International Renewable and Sustainable Energy Conference (IRSEC).
  15. Bordon, J., Moskon, M., Zimic, N., and Mraz, M. 2015. Fuzzy logic as a computational tool for quantitative modelling of biological systems with uncertain kinetic data. IEEE/ACM Trans Comput Biol Bioinform 12(5):1199–1205.
  16. Liu, F., Heiner, M., and Gilbert, D. 2020. Fuzzy Petri nets for modelling of uncertain biological systems. Brief. Bioinformatics 21(1):198–210.
  17. Liu, F., Sun, W., Heiner, H., and Gilbert, G. 2021. Hybrid modelling of biological systems using fuzzy continuous Petri nets. Brief. Bioinformatics 22(1):438–450.
  18. Zhu, X.L., Jiang, Z.Y., Wang, B., and He Y.J. 2018. Decoupling control based on fuzzy neural-network inverse system in marine biological enzyme fermentation process. IEEE Access, 6:36168–36175.
  19. Wu, S. J. and Lin, C. T. 2000. Optimal fuzzy controller design: local concept approach. IEEE Trans. Fuzzy Systems 8(2):171-185.
  20. Wu, S. J. 2008. Reply to “Further comment on “optimal fuzzy controller design: local concept approach””. IEEE Trans. Fuzzy Systems 16(2):547-549.
  21. Åström, K.J., Hägglund, T. 1995. PID controllers: theory, design and tuning. Instrument Society of America, North Carolina.
  22. Zhuang, M. and Atherton, D.P. 1993. Automatic tuning of optimum PID controllers. In proceeding of the IEE D.
  23. Mudi, R.K. and Pal, N.R. 1999. A robust self-tuning scheme for PI- and PD-type fuzzy controllers. IEEE Trans. Fuzzy Syst. 7(1):2–16.
  24. Li, T.H.S. and Shieh, M.Y. 2000. Design of a GA-based fuzzy PID controller for nonminimum phase systems. Fuzzy Sets Syst. 111(2):183–197.
  25. Liu, Y., Jiang, D., Yun, J., Sun, Y., Li, C., Jiang, G., Kong, J., Tao, B. and Fang, Z. 2022. Self-tuning control of manipulator positioning based on Fuzzy PID and PSO algorithm. Front. Bioeng. Biotechnol. 9:817723.
  26. Chao, C. T., Sutarna, N., Chiou, J. S. and Wang, C. J. 2019. An optimal fuzzy PID controller design based on conventional PID control and nonlinear factors. Appl. Sci. 9:1224.
  27. Elsrogy, W. M., Fkirin, M. A. and Hassan, M. A. M. 2013. Speed control of DC motor using PID controller based on artificial intelligence techniques. In Proceedings of the 2013 CoDIT.
  28. Carvajal, J., Chen, G. and Ogmen, H. 2000. Fuzzy PID controller: design, performance evaluation, and stability analysis. Inf. Sci. 123(3-4):249-270.
  29. Hermassi, M., Krim, S., Kraiem, Y., Hajjaji, M.A., Alshammari, B.M., Alsaif, H., Alshammari, A.S. and Guesmi, T. 2023. Design of vector control strategies based on fuzzy gain scheduling PID controllers for a grid-connected wind energy conversion system: hardware FPGA-in-the-loop verification. Electronics 12:1419.
  30. Chaiyatham, T. and Ngamroo, I. 2013. Optimal fuzzy gain scheduling of PID controller of superconducting magnetic energy storage for power system stabilization. Int. J. Innov. Comput. Inf. Control. 9:651–666.
  31. Phu, N.D., Hung, N.N., Ahmadian, A. and Senu, N. 2020. A new fuzzy PID control system based on fuzzy PID Controller and fuzzy control process. Int. J. Fuzzy Syst. 22:2163–2187.
  32. Cao, K., Gao, X., Lam, H. K. and Vasilakos, A. 2016.
Index Terms

Computer Science
Information Sciences


T-S Fuzzy systems pole-placement optimal fuzzy control