Echo state network optimization using binary grey wolf algorithm

Liu, J, Sun, T, Luo, Y, Yang, S, Cao, Y and Zhai, J ORCID: https://orcid.org/0000-0002-2746-7749 2020, 'Echo state network optimization using binary grey wolf algorithm' , Neurocomputing, 385 , pp. 310-318.

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Abstract

The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error.

Item Type: Article
Schools: Schools > Salford Business School
Journal or Publication Title: Neurocomputing
Publisher: Elsevier
ISSN: 0925-2312
Related URLs:
Depositing User: J Zhai
Date Deposited: 20 Feb 2020 08:49
Last Modified: 12 Mar 2020 15:15
URI: http://usir.salford.ac.uk/id/eprint/56361

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