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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Junfei Qiao, Fei Li, Cuili Yang, Wenjing Li and Ke Gu, "A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 276-291, Jan. 2020. doi: 10.1109/JAS.2019.1911852
Citation: Junfei Qiao, Fei Li, Cuili Yang, Wenjing Li and Ke Gu, "A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 276-291, Jan. 2020. doi: 10.1109/JAS.2019.1911852

A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

doi: 10.1109/JAS.2019.1911852
Funds:  This work was supported by the National Natural Science Foundation of China (61890930-5, 61533002, 61603012), the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), the National Key Research and Development Project (2018YFC1900800-5), and Beijing Municipal Education Commission Foundation (KM201710005025)
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  • Radial basis function neural network (RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm (DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength (IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm (DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

     

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