Double echo state network with multiple reservoirs for time-series prediction

Hongchuang Zhang; Shuxian Lun; Chang Liu; Zhenduo Sun

doi:10.1117/12.2626677

16 December 2021 Double echo state network with multiple reservoirs for time-series prediction

Hongchuang Zhang, Shuxian Lun, Chang Liu, Zhenduo Sun

Proceedings Volume 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021); 1215309 (2021) https://doi.org/10.1117/12.2626677
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 2021, Sanya, China

Abstract

With the rise of neural networks, Echo state network (ESN) is also added to the queue of the neural network. ESN is a powerful form of reservoir computation, is a new type of recurrent neural network and only need to train the output weights. ESN overcomes the problems of the traditional recursive neural network such as too complicated training algorithm, slow convergence speed and easy to fall into local minimum.In order to reflect different learning tasks need different status updating methods and appropriate reservoir are constructed for a given task, an improved leaky integrator echo state network is proposed. The improved model, named Double Echo State Network with Multiple Reservoir(DMESN) for Time-Series Prediction. It uses multiple sub-reservoir to build a total reservoir and extends it in depth. Then the batch gradient descent method is utilized to optimize the DM-ESN parameters. Finally, the proposed method is applied to the time-series prediction. Simulation results show that the DM-ESN model can greatly improve the accuracy and stability.

Citation Download Citation

Hongchuang Zhang, Shuxian Lun, Chang Liu, and Zhenduo Sun "Double echo state network with multiple reservoirs for time-series prediction", Proc. SPIE 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 1215309 (16 December 2021); https://doi.org/10.1117/12.2626677

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