Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO
Automation, Control and Intelligent Systems
Volume 4, Issue 6, December 2016, Pages: 84-88
Received: Nov. 29, 2016; Published: Dec. 1, 2016
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Xiaodong Zhang, Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China
Xuefeng Yan, Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China
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The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.
Echo State Network, Reservoir Computing, Artificial Neural Network, Ensemble Learning, Selective Ensemble, Particle Swarm Optimization
To cite this article
Xiaodong Zhang, Xuefeng Yan, Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO, Automation, Control and Intelligent Systems. Vol. 4, No. 6, 2016, pp. 84-88. doi: 10.11648/j.acis.20160406.11
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