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Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization

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  • Intelligent Control and Applications
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Abstract

An Improved Ant Colony Optimization (IACO) is proposed to design A2-C1 type fuzzy logic system (FLS) in the paper. The design includes parameters adjustment and rules selection, and the performance of the intelligent fuzzy system, which can be improved by choosing the most optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed algorithm, the intelligence fuzzy logic systems based on the algorithms are applied to predict the Mackey-Glass chaos time series. The simulations show that both the IACO and ACO have better tracking performances. The results compared with classical algorithm BP ( back-propagation design) shows the tracking performance of IACO is more precise, the result compared with ACO shows that either the training result or the testing result, the tracking performance of IACO is better, and IACO has a faster convergence rate than ACO, the results compared with the Intelligent type-1 fuzzy logic systems show that both the A2-C1 type FLS based on IACO and ACO have better tracking performance than type-1 fuzzy logic system.

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, China

    Zhifeng Zhang, Tao Wang, Yang Chen & Jie Lan

Authors
  1. Zhifeng Zhang
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  2. Tao Wang
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  3. Yang Chen
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  4. Jie Lan
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Corresponding author

Correspondence to Tao Wang.

Additional information

Recommended by Associate Editor Myung Geun Chun under the direction of Editor Euntai Kim. This work is supported by the National Natural Science Foundation of China (61374113), and by Liaoning Province College Basic Scientific Research Business Funding Project (JL201615410),Liaoning Province Natural Science Foundation Guidance Project (No. 20180550056).

Zhifeng Zhang received the B.S. degree in Information and Computing Science from Liaoning University of Technology, Jinzhou, China, in 2015. Currently, he is pursuing an M.S. degree in applied mathematics in College of Science, Liaoning University of Technology. His current research interests include include fuzzy reasoning and fuzzy control, type-2 fuzzy logic systems, system identification and fuzzy neural networks.

Tao Wang received her B.S. degree from Northeastern Normal University, Changchun, China, and her M.S. degree from Liaoning Normal University, Dalian, China, in 1988 and 2000, respectively. Currently, she is a professor in College of Science, Liaoning University of Technology. Her current research interests include fuzzy reasoning and fuzzy control, type-2 fuzzy logic systems, fuzzy neural networks and system identification.

Yang Chen received his B.S. degree in Information and Computing Science and his M.S. degree in applied mathematics from Liaoning University of Technology, Jinzhou, China, in 2004 and 2007, respectively. He is pursuing a Ph.D. degree in College of Information Science and Engineering, Northeastern University. He is currently a lecturer in College of Science, Liaoning University of Technology. His current research interests include fuzzy reasoning and fuzzy control, type-2 fuzzy logic systems, system identification and permanent magnetic drive.

Jie Lan received her B.S. degree from Jilin Agricultural University, Changchun, China, and her M.S. degree in applied mathematics from Liaoning University of Technology, Jinzhou, China, in 2009 and 2012, respectively. She is currently a lecturer in College of Science, Liaoning University of Technology. Her current research interests include fuzzy reasoning and fuzzy control, type-2 fuzzy logic systems, fuzzy neural networks and system identification.

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Zhang, Z., Wang, T., Chen, Y. et al. Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization. Int. J. Control Autom. Syst. 17, 536–544 (2019). https://doi.org/10.1007/s12555-017-0451-1

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  • DOI: https://doi.org/10.1007/s12555-017-0451-1

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