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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Yang Yu, Zhenyu Lei, Yirui Wang, Tengfei Zhang, Chen Peng and Shangce Gao, "Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 99-110, Jan. 2022. doi: 10.1109/JAS.2021.1004284
Citation: Yang Yu, Zhenyu Lei, Yirui Wang, Tengfei Zhang, Chen Peng and Shangce Gao, "Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 99-110, Jan. 2022. doi: 10.1109/JAS.2021.1004284

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

doi: 10.1109/JAS.2021.1004284
Funds:  This work was partially supported by the National Natural Science Foundation of China (62073173, 61833011), the Natural Science Foundation of Jiangsu Province, China (BK20191376), and the Nanjing University of Posts and Telecommunications (NY220193, NY220145)
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  • Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and easily dropping into local minima. Therefore, more and more research adopts non-BP learning algorithms to train ANNs. In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima. The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem. Nine meta-heuristic algorithms are applied into comparison, including the champion of the 2017 IEEE Congress on Evolutionary Computation (CEC2017) benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR). The experimental results reveal that DSNDE achieves better performance than its peers.

     

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    Highlights

    • The optimization performance of DE is enhanced by a dynamic scale-free network
    • DSNDE is developing as a learning algorithm to improve the performance of DNM
    • The DSNDE-trained DNM has high accuracy in prediction, classification and other issues

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