4.6 Article

A self-organizing recurrent fuzzy neural network based on multivariate time series analysis

Journal

NEURAL COMPUTING & APPLICATIONS
Volume 33, Issue 10, Pages 5089-5109

Publisher

SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05276-w

Keywords

Self-organizing recurrent fuzzy neural network; Multivariate time series analysis; Prediction; Wastewater

Funding

  1. National Natural Science Foundation of China [618909305, 61533002, 61603009]
  2. National Key Research and Development Project [2018YFC1900800-5]
  3. Beijing Natural Science Foundation [4182007]
  4. Beijing Municipal Education Commission Foundation [KM201910005023]
  5. Major Science and Technology Program for Water Pollution Control and Treatment of China [2018ZX07111005]

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The paper proposes a self-organizing recurrent fuzzy neural network based on multivariate time series analysis, which utilizes a recurrent mechanism and a self-organization mechanism to optimize network structure. The theoretical analysis of its convergence and practical validation demonstrate its effectiveness in modeling nonlinear systems.
Fuzzy neural networks (FNNs) have attracted considerable interest for modeling nonlinear dynamic systems in recent years. However, the recurrent design and the self-organizing design of FNNs generally lack adaptability, and their analyses on the change rule of networks in continuous time are insufficient. To solve these problems, a self-organizing recurrent fuzzy neural network based on multivariate time series analysis (SORFNN-MTSA) is proposed in this paper. First, a recurrent mechanism, based on wavelet transform fuzzy Markov chain algorithm, is introduced to obtain adaptive recurrent values and accelerate convergence speed of the network. Second, a self-organization mechanism, based on weighted dynamic time warping algorithm and sensitivity analysis algorithm, is presented to optimize the network structure. Third, the convergence of SORFNN-MTSA is theoretically analyzed to show the efficiency in both fixed structure and self-organizing structure cases. Finally, several benchmark nonlinear systems and a real application of wastewater treatment are used to verify the effectiveness of SORFNN-MTSA. Compared with other existing methods, the proposed SORFNN-MTSA performs better in terms of both high accuracy and compact structure.

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