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An online adjusting RBF neural network for nonlinear system modeling

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Abstract

Aiming to improve the prediction accuracy and to obtain a compact structure, an online adjusting radial basis function neural network (OA-RBFNN) is proposed in this paper. The proposed OA-RBFNN realizes online modeling by combining the advantages of the sliding window strategy and clustering algorithm. First, with a small batch of samples in the first sliding window, the network is initialized using a density canopy-based k-means algorithm, and an optimal network structure and its initial parameters are determined automatically. Secondly, through the sliding window movement, the network parameters are adjusted by fine-tuning based on the changed samples, followed by the gradient-based online learning algorithm. Finally, the effectiveness of the proposed OA-RBFNN model is verified by four experiments: the function approximation, Mackey–Glass time series prediction, Lorenz time series prediction, and the effluent BOD prediction in wastewater treatment plant (WWTP), and the prediction accuracy obtained in these four experiments reached 97.11%, 99.25%, 99.69%, and 98.81%, respectively. The results demonstrate that the OA-RBFNN can achieve competitive prediction performance while having a more compact network structure than the existing online RBF neural networks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Nos. 62021003, 61890930-5, and 62173008]; National Key Research and Development Project [No. 2018YFC1900800-5]; Beijing Municipal Education Commission Foundation [No. KM201910005023] and National Key Research and Development Program of China [2021ZD0112301].

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Lijie Jia, Wenjing Li & Junfei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, 100124, Beijing, China

    Lijie Jia, Wenjing Li & Junfei Qiao

  3. Beijing Laboratory for Smart Environmental Protection, 100124, Beijing, China

    Lijie Jia, Wenjing Li & Junfei Qiao

  4. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, 100124, Beijing, China

    Lijie Jia, Wenjing Li & Junfei Qiao

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  1. Lijie Jia
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  2. Wenjing Li
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Correspondence to Junfei Qiao.

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Jia, L., Li, W. & Qiao, J. An online adjusting RBF neural network for nonlinear system modeling. Appl Intell 53, 440–453 (2023). https://doi.org/10.1007/s10489-021-03106-7

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