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FLOWFS: Fast Learning-algorithm with Optimal Weights for Fuzzy Systems

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Abstract

Although the Wang–Mendel (WM) method, a typical way of fuzzy modeling, can rapidly obtain fuzzy rules from data to construct fuzzy systems with good interpretability, its accuracy is not high. In the literatures, many optimization approaches are proposed to improve the accuracy of the WM method, but the optimization process is in general time-consuming. A shallow or single fuzzy system cannot deal with “the curse of dimensionality”, which makes it challenging to realize modeling of high-dimensional data. Inspired by deep neural networks, building deep fuzzy systems (DFS) through many shallow fuzzy systems will become a new direction of fuzzy modeling for high-dimensional data modeling. As a building block in DFS, each shallow fuzzy system should have the characteristics of high precision and fast training speed to ensure the operating efficiency of DFS. This paper proposes a Fast Learning-algorithm with Optimized Weights for Fuzzy Systems (FLOWFS) in the following three steps: (1) Obtain a basic fuzzy system with full rules through fast training; (2) Assign each fuzzy rule a weight starting with 1; (3) Develop a fast learning-algorithm optimal weights via the least square method and coefficient regularization. Aiming at the regression problems, FLOWFS is compared with the back propagation neural network (BP), radial basis function neural network (RBF), the WM method and long short-term memory (LSTM) on three classic datasets of UCI. The experimental results show that: (1) FLOWFS has achieved a higher prediction accuracy; (2) Compared with the WM method, FLOWFS not only has greater accuracy, but also is faster in training; (3) In terms of comprehensive indicators of accuracy and running-time, FLOWFS has the best performance. Therefore, FLOWFS can provide good fuzzy building blocks for DFS to enable fast modeling of high-dimensional data and achieve good interpretability and high accuracy.

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Acknowledgements

Open Project Support of State Key Laboratory for Management and Control of Complex Systems (20210116). The authors would like to thank the anonymous referees for their invaluable insights, and the research work was jointly by grants from the National Natural Science Foundation of China (No. 61976055); Special fund for education and scientific research of Fujian Provincial Department of Finance (GY-Z21001).

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Authors and Affiliations

  1. School of Transportation, Fujian University of Technology, Fuzhou, 350118, China

    Dewang Chen

  2. The College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Wenlin Tong & Yunhu Huang

  3. Key Laboratory of Intelligent Metro of Universities in Fujian Province, Fuzhou University, Fuzhou, 350108, China

    Wenlin Tong & Yunhu Huang

  4. Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202-5132, USA

    Lingxi Li

  5. MBA Program, School of Management, Shanghai University, Shanghai, 20044, China

    Jing Zhang

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  1. Dewang Chen
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  2. Wenlin Tong
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  3. Yunhu Huang
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Correspondence to Yunhu Huang.

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Chen, D., Tong, W., Huang, Y. et al. FLOWFS: Fast Learning-algorithm with Optimal Weights for Fuzzy Systems. Int. J. Fuzzy Syst. 24, 3162–3173 (2022). https://doi.org/10.1007/s40815-022-01329-5

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  • DOI: https://doi.org/10.1007/s40815-022-01329-5

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