An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification

• Published in: International journal of automation and computing Vol. 11; no. 4; pp. 434 - 440
• Main Authors: Xu, Bao-Chang, Zhang, Ying-Ying
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.08.2014; Springer Nature B.V
• Subjects: algorithm; Algorithms; Artificial neural networks; Artificial satellites; Automation; Back propagation networks; CAE) and Design; change; Computer Applications; Computer-Aided Engineering (CAD; Control; Dynamics; Engineering; Genetic algorithms; Gravitational; Identification; Magnetic levitation; Mathematical models; Mechatronics; network; neural; Neural networks; Optimization; Optimization algorithms; orbital; Performance enhancement; Random variables; Recurrent neural networks; Regular Paper; Robotics; search; Search algorithms; Search process; Searching; Simulation; system; System identification; Trial and error methods; Gravitational search algorithm; system identification; optimization; neural network; orbital change
• ISSN: 1476-8186; 2153-182X; 1751-8520; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-014-0810-9

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.