Volume 11 Number 4
August 2014
Article Contents
Bao-Chang Xu and Ying-Ying Zhang. An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification. International Journal of Automation and Computing, vol. 11, no. 4, pp. 434-440, 2014. doi: 10.1007/s11633-014-0810-9
Cite as: Bao-Chang Xu and Ying-Ying Zhang. An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification. International Journal of Automation and Computing, vol. 11, no. 4, pp. 434-440, 2014. doi: 10.1007/s11633-014-0810-9

An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification

  • Received: 2013-05-27
Fund Project:

This work was supported by National Natural Science Foundation of China (No. 2011ZX05021-003) and Science Foundation of China University of Petroleum.

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Abstract Views (4774) PDF downloads (3637) Citations (0)

An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification

Fund Project:

This work was supported by National Natural Science Foundation of China (No. 2011ZX05021-003) and Science Foundation of China University of Petroleum.

Abstract: Gravitational search algorithm (GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm (IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent's position further using the coordinate descent method. For the experimental verification of the proposed algorithm, both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous (NARX) recurrent neural network identification for a magnetic levitation system. Compared with the system identification based on gravitational search algorithm neural network (GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.

Bao-Chang Xu and Ying-Ying Zhang. An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification. International Journal of Automation and Computing, vol. 11, no. 4, pp. 434-440, 2014. doi: 10.1007/s11633-014-0810-9
Citation: Bao-Chang Xu and Ying-Ying Zhang. An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification. International Journal of Automation and Computing, vol. 11, no. 4, pp. 434-440, 2014. doi: 10.1007/s11633-014-0810-9
Reference (18)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return