Artificial intelligence

Abstract: The aim is to create an intelligent control system based on soft computing for controlling dynamic objects moving along one of the defined routes in real-time systems. The parameters of objects in the real environment are characterized by high nonlinearity, dependence on the state of the environment, and time-varying dynamics when some parameters and states of objects are not available for measurement. Taking this into account, the hierarchical structure of the system is developed based on the classical fuzzy algorithms of Mamdani and Takagi-Sugeno-Kang, and an adaptive fuzzy neural network that implements the model. The application of a neuro-fuzzy model to controlling the movement of dynamic objects with many parameters and incomplete certainty through the use of expert knowledge is considered. A mathematical description of the fuzzy hierarchical model, a learning algorithm, and computer modeling are presented on the example of controlling the speed of rolling cars from a sorting hill. An example of the application of fuzzy rules built on numerical data is considered. The results of modeling with visualization of the results for the synthesized data are presented. The scientific innovation of the obtained results lies in the development of a hierarchical neuro-fuzzy model designed for forecasting and controlling dynamic objects. The modeling results confirm the ability of the proposed model to predict the unknown mapping of the input data vector, which consists of measured and unmeasured parameters, into the desired numerical value at certain points of the path at the model output. The obtained results demonstrate effective prediction of motion dynamics, the ability to achieve high forecasting accuracy and the possibility of intellectualizing the control of the technological process. When approximating the nonlinear dependence, the use of a multilayer neural network ensures the adaptability of the model to a specific area of application, and synergy with a fuzzy algorithm allows automating the process of controlling the technological process at the level of a human operator.

Keywords: adaptive control, fuzzy neural networks, hierarchical structure, Sugeno knowledge base, TSK algorithm, Mamdani algorithm, ANFIS, M-ANFIS

References:

1. Takagi T. & Sugeno M. (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15:116–132.
2. Sugeno M., editor. (1985) Industrial applications of fuzzy control. Elsevier Science Pub. Co.
3. Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems Man & Cybernetics, 23, 665-685. https://doi.org/10.1109/21.256541.
4. Chai Y., Jia L., Zhang Z. (2009) Mamdani model based adaptive neural fuzzy inference system and its application. International Journal of Computer, Electrical, Automation, Control and Information Engineering 3(3).
5. Takagi H. & Hayashi I. (1988) Artificial neural network-driven fuzzy reasoning, Proc. Int. Workshop Fuzzy System Applications (IIZUKA88), Iizuka, Japan, 217C218.
6. Jang, J-S & Sun, C-T. (1993). Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems. IEEE Transaction on Neural Networks, 4(1), 156-159.
7. Gite, A.V., Bodade, R.M., & Raut, B.M. (2013). ANFIS controller and its application. International Journal of Engineering Research and Technology, 2(2).
8. Abraham, A. (2005). Adaptation of fuzzy inference system using neural learning. In Fuzzy Systems Engineering: Theory and Practice, pp. 53-83. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11339366_3, ISBN 978-3-540-25322-8.
9. Riza L.S., Bergmeir C., Herrera F., & Benítez J.M. (2015). frbs: Fuzzy rule-based systems for classification and regression in R. Journal of statistical software, 65, 1-30.
10. Raja P., & Pahat B. (2016). A review of training methods of ANFIS for applications in business and economics. International Journal of u-and e-Service, Science Technology, 9(7), 165-172.
11. Mehran K. (2008). Takagi-sugeno fuzzy modeling for process control. Industrial Automation, Robotics and Artificial Intelligence (EEE8005), 262, 1-31.
12. Schnitman L., & Yoneyama T. (2001). Takagi-Sugeno-Kang fuzzy structures in dynamic system modeling. In Proceedings of the IASTED International Conference on Control and Application (CA’2001), 160-165.
13. Jang J.S.R., Sun C.T., & Mizutani E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 1482-1484.
14. Lazarieva N. (2023) Modeling of multi-factory dependences in complex control systems by Sugeno fuzzy knowledge base. Artificial Intelligence 1, 138-146.
15. Mamdani E.H. (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, C-26(12), 1182-1191, doi: 10.1109/TC.1977.1674779.
16. Lazarieva N.M., Lazariev O.V. (2023) Creating of a rule base according to numerical data for the fuzzy control system of retarders. Х Vseukrainska naukovo-praktychna konferentsiia zdobuvachiv vyshchoi osvity ta molodykh vchenykh z avtomatychnoho upravlinnia Kherson-Khmelnytskyi: KhNTU, 78-81.