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Smart Structures and Systems
  Volume 26, Number 6, December 2020 , pages 797-807

System simulation and synchronization for optimal evolutionary design of nonlinear controlled systems
C.Y.J. Chen, D. Kuo, Chia-Yen Hsieh and Tim Chen

    Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.
Key Words
    system simulation and synchronization; fuzzy relaxed system; intelligent algorithm
(1) C.Y.J. Chen:
Faculty of Information Technology, University of California, Irvine, USA;
(2) D. Kuo:
Faculty of Engineering, University of Melbourne, Melbourne, Victoria, Australia;
(3) Chia-Yen Hsieh:
Scientific Education, National Kaohsiung Normal University, Kaohsiung, Taiwan;
(4) Tim Chen:
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

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