Echo State Network Optimization using Hybrid-Structure Based Gravitational Search Algorithm with Square Quadratic Programming for Time Series Prediction

  • Ghadeer Written by
  • Update: 29/06/2022

Echo State Network Optimization using Hybrid-Structure Based Gravitational Search Algorithm with Square Quadratic Programming for Time Series Prediction

Zohaib Ahmad

School of Electronics and

Information Engineering,

Beijing University of Technology,

China

This email address is being protected from spambots. You need JavaScript enabled to view it.

Muhammad Qasim Memon

Department of Information

and Computing,

University of Sufism and Modern Sciences,

Pakistan

This email address is being protected from spambots. You need JavaScript enabled to view it.

Aasma Memon

School of Management

and Economics,

Beijing University of Technology,

China

This email address is being protected from spambots. You need JavaScript enabled to view it.

Parveen Munshi

Faculty of Education,

University of Sufism and Modern Sciences,

Pakistan

This email address is being protected from spambots. You need JavaScript enabled to view it.

Muhammad jaffar Memon

Civil Engineering Department, SZAB Campus,

Mehran University of Engineering and Technology,

Pakistan

This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract: The Echo-State Network (ESN) is a robust recurrent neural network and a generalized form of classical neural networks in time-series model designs. ESN inherits a simple approach for training and demonstrates the high computational capability to solve non-linear problems. However, input weights and the reservoir's internal weights are pre-defined when optimizing with only the output weight matrix. This paper proposes a Hybrid Gravitational Search Algorithm (HGSA) to compute ESN output weights. In Gravitational Search Algorithm (GSA), Square Quadratic Programming (SQP) is united as a local search strategy to raise the standard GSA algorithm's efficiency. Later, an HGSA-SQP and the validation data set to establish the relation configuration of the ESN output weights. Experimental results indicate that the proposed configuration of HGSA-SQP-ESN is more efficient than the other conventional models of ESN with the minimum generalization error.

Keywords: Echo state network, hybrid gravitational search algorithm, network configuration optimization, time series prediction.

Received April 10, 2022; accepted April 28, 2022

https://doi.org/10.34028/iajit/19/3A/13

Full text

Read 543 times
Top
We use cookies to improve our website. By continuing to use this website, you are giving consent to cookies being used. More details…