论文信息 - Modeling of electro-hydraulic position servo system of pump-controlled cylinder based on HHGA-RBFNN

Modeling of electro-hydraulic position servo system of pump-controlled cylinder based on HHGA-RBFNN

To solve the problem that it is difficult to construct an exact mathematic model for the electro-hydraulic position servo system of a pump-controlled cylinder with nonlinearity and time-varying property, HHGA-RBFNN is proposed. Each chromosome only contains three parameters including the number of hidden nodes, center and width of radial basis function; so that the complexity of proposed algorithm could be decreased with the optimal solution converged. By coding the center and width of the radial basis function respectively, the convergence rate of the algorithm could be improved; by considering both the number of hidden nodes and root-mean-square error for the selection of fitness function, the structure and parameters of network are optimized simultaneously. The simulation and experimental results show that the proposed algorithm has high modeling accuracy, excellent generalization and fast convergence rate. The simulation and experimental results show that the proposed algorithm is valid and accurate.

Jin Yong | Gao Qiang | Tong Zhong-zhi | Ji Li-jun | Hou Yuan-long

[1] Ping Chen,et al. Approaches to non-linearity compensation of pressure transducer based on HGA-RBFNN , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[2] Panagiotis Patrinos,et al. A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models , 2005 .

[3] Weiwei Liu,et al. An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction , 2006 .

[4] Whei-Min Lin,et al. A Fault Classification Method by RBF Neural Network with OLS Learning Procedure , 2001 .

[5] Juan Julián Merelo Guervós,et al. Evolving RBF neural networks for time-series forecasting with EvRBF , 2004, Inf. Sci..

[6] Zhang Caiqing,et al. Comparing BP and RBF Neural Network for Forecasting the Resident Consumer Level by MATLAB , 2008, 2008 International Conference on Computer and Electrical Engineering.

[7] Guoyu Zuo,et al. Genetic algorithm based RBF neural network for voice conversion , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[8] Mita Nasipuri,et al. Improved k-means algorithm in the design of RBF neural networks , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.