Parametric Optimization of the Hierarchical Fuzzy Model of Control with Transfer of Fuzzy Values of Intermediate Data
DOI:
https://doi.org/10.18372/1990-5548.84.20190Keywords:
neural network, fuzzy algorithm, gradient descent, model training, parameter adaptationAbstract
The subject of the study is the intellectualization of the technological process of controlling complex objects in order to intellectualize and replace the labor of a human operator. In conditions that are difficult to describe by mathematical methods due to incompleteness and uncertainty, a hybrid neuro-fuzzy model with a hierarchical structure is used to control the process. The aim of the article is to study and develop a learning algorithm for the Mamdani→Sugeno model with the transfer of fuzzy intermediate data between hierarchical levels, implemented by an adaptive neural network. To ensure the accuracy of real-time forecasting, an algorithm for parametric adaptation to operating conditions with the adjustment of the parameters of antecedents and consequences at two levels has been defined. When studying the methods of data transfer between levels, fuzzy logic and artificial neural networks methods, the gradient descent method, Mamdani and Takagi–Sugeno–Kang algorithms, etc. were used. The study confirms the possibility of using hybrid models to intellectualize the process of controlling complex objects. The scientific innovation of the obtained results is the construction of a neural network of a hierarchical control system and the development of a learning algorithm for the transfer of fuzzy intermediate variables with parametric model adaptation based on the gradient descent algorithm.
References
N. Takagi & I. Hayashi, “Artificial neural network-driven fuzzy reasoning,” Proc. Int. Workshop Fuzzy System Applications (IIZUKA88), Iizuka, Japan, 217C218, 1988.
J-S Jang & C-T Sun, “Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems,” IEEE Transaction on Neural Networks, vol. 4, no. 1, pp. 156–159, Jan. 1993. https://doi.org/10.1109/72.182710
J.-S.R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, May-June 1993, pp. 665–685. https://doi.org/10.1109/21.256541
Shu-Chieh Chang, “Adaptive nonlinear control using fuzzy logic and neural networks,” 1994, Dissertations.
Y. Chai, L. Jia, and Z. Zhang, “Mamdani model based adaptive neural fuzzy inference system and its application,” International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 3, no. 3, 2009. https://doi.org/10.1109/FSKD.2009.76
A. Abraham, “Adaptation of fuzzy inference system using neural learning,” Fuzzy Systems Engineering, vol. 181, 2005, ISBN: 978-3-540-25322-8.
S. Shtovba, “Lohichne vyvedennya za iyerarkhichnymy hibrydnymy nechitkymy bazamy znanʹ,” Obchyslyuvalʹnyy intelekt (rezulʹtaty, problemy, perspektyvy). Materialy konferentsiyi. 2013, pp. 128–131. [in Ukrainian]
P. N. Ekemezie and C. C. Osuagwu, “A self organising fuzzy logic controller,” Nigerian Journal of Technology, vol. 20, no. 1 March, 2001.
N. Lazarieva, “Modeling of multi-factory dependences in complex control systems by Sugeno fuzzy knowledge base,” Artificial Intelligence, no. 1, pp. 138–146, 2023. https://doi.org/10.15407/jai2023.01.138.
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