Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm
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This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems.
KeywordsMultilayer fuzzy model Cascade training Differential evolution (DE) algorithm Nonlinear double-coupled fluid tank system Multiple-inputs multiple-outputs (MIMO) system Fuzzy Takagi–Sugeno (T–S) model
This research is fully funded by Viet Nam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 107.01-2015.23, Viet Nam.
- 6.Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M.: New method for fuzzy nonlinear modelling based on genetic programming. Lecture notes in computer science] artificial intelligence and soft computing, vol. 9692, no. 38, pp. 432–449 (2016)Google Scholar
- 9.Ferdaus, M.M. et al. Fuzzy clustering based nonlinear system identification and controller development of Pixhawk based quadcopter. In: Advanced Computational Intelligence (ICACI), 2017 Ninth International Conference on. IEEE, pp. 223–230 (2017)Google Scholar
- 13.Sun, L., Huo, W. Adaptive fuzzy control of spacecraft proximity operations using hierarchical fuzzy systems. In: IEEE/ASME Transactions on Mechatronics, vol. 4435, no. c, pp. 1–1 (2015)Google Scholar
- 17.Al-Hmouz, R., Pedrycz, W., Balamash, A., Morfeq, A.: Hierarchical system modeling. IEEE Trans. Fuzzy Syst. PP(99), 1–1 (2017)Google Scholar
- 23.Kien, C.V., Son, N.N., Anh, H.P.H.: Identification of 2-DOF pneumatic artificial muscle system with multilayer fuzzy logic and differential evolution algorithm. In: The 12th IEEE Conference on Industrial Electronics and Applications (ICIEA 2017), June 18–20, Siem Reap, Cambodia, pp. 1261–1266Google Scholar