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Emotional Neural Network Based on Improved CLPSO Algorithm For Time Series Prediction

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Abstract

In recent years, emotional neural networks (ENNs) have been extensively used in the field of time series prediction. As a variant of ENN, the radial basis emotional neural network (RBENN) is chosen as the prediction model of time series in this paper, because it has a special type of structure that can preprocess the interference in the data. However, it is difficult for many existing methods to determine network structure automatically while adjusting network parameters. To solve this problem, an RBENN based on adaptive inertia weight comprehensive learning particle swarm optimization algorithm (ADw-CLPSO-RBENN) is designed. Firstly, an adaptive inertia weight adjustment strategy based on the CLPSO algorithm (ADw-CLPSO) is exploited to balance the global and local search ability of particles. Secondly, a particle-variable dimensional learning mechanism (PVDLM) is developed based on the ADw-CLPSO algorithm, which enables particles to find the appropriate network structure while searching for the optimal parameter solution. Finally, the proposed method is evaluated in two time series and a real wastewater treatment system. The simulation results demonstrate that the proposed ADw-CLPSO-RBENN can automatically adjust to a suitable network structure, and the prediction accuracy is also better than other methods. Therefore, the proposed method has higher superiority in time series prediction.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61533002, 61890930 and 61973010, the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), and the National Key Research and Development Project under Grants 2018YFC1900800-5.

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  1. Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

    Hongye Zhang, Cuili Yang & Junfei Qiao

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Correspondence to Junfei Qiao.

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Appendix: Parameters Setting for ENN-GA, BELPR and WTAENN

Appendix: Parameters Setting for ENN-GA, BELPR and WTAENN

For the ENN-GA, the parameters of GA are given: the size of population is 4000 and the maximum generation is 4000. For BELPR, the method of initializing the weights V and W of the AMYG and OFC neurons is the same as in the original literature [11], in which V is randomly initialized in the range of [0, 1] and W is its opposite vector. In addition, the learning rate varies from 0.05 to 0.8 by step of 0.01, the decay rate varies from 0.001 to 0.01 by step of 0.001. For WTAENN, the number of competitive parts is 1, the parameters of the GA are given as: the size of population is 100 and the maximum generation is 400.

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Zhang, H., Yang, C. & Qiao, J. Emotional Neural Network Based on Improved CLPSO Algorithm For Time Series Prediction. Neural Process Lett 54, 1131–1154 (2022). https://doi.org/10.1007/s11063-021-10672-x

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