Abstract
In this paper, a new cascade training Multilayer Fuzzy logic is proposed for identifying the forward model of double-coupled tank system based on experiment. The Multilayer Fuzzy model consists of multiple MISO models; for each MISO model, it consists of multiple single Fuzzy T-S models. The cascade training optimized with DE algorithm sequentially trained Multilayer Fuzzy model one by one. All parameters of the model are optimally trained with differential evolution (DE) algorithm. The experimental results show that proposed method gives better performance than the normal training. This proposed method can be used for optimal Multilayer Fuzzy logic that efficiently applied for identifying nonlinear MISO and MIMO systems. The experimental cascade training tests are presented. It proves more accuracy and takes less time to compute than the normal training method and demonstrates promisingly scalable and simple method as to successfully identify nonlinear uncertain MIMO system.
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Acknowledgments
This paper is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2015.23 and by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2016-20-03.
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Van Kien, C., Anh, H.P.H. (2018). Cascade Training Multilayer Fuzzy Model for Identifying Nonlinear MIMO System. In: Nguyen-Xuan, H., Phung-Van, P., Rabczuk, T. (eds) Proceedings of the International Conference on Advances in Computational Mechanics 2017. ACOME 2017. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-7149-2_71
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DOI: https://doi.org/10.1007/978-981-10-7149-2_71
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