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The System Identification for the Hydrostatic Drive System of Secondary Regulation Using Neural Networks
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English
Abstract
In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.
Authors
Citation
Wu, G., Liu, Q., Zhao, K., Li, S. et al., "The System Identification for the Hydrostatic Drive System of Secondary Regulation Using Neural Networks," SAE Technical Paper 962231, 1996, https://doi.org/10.4271/962231.Also In
Issues in Commercial Vehicle Powertrain Design and Development
Number: SP-1203; Published: 1996-10-01
Number: SP-1203; Published: 1996-10-01
References
- Luo Bangjie Wu Guangqiang On Dynamic Characteristics of Passenger Car Powertrain with a Hydromechanical Transmission SAE 932918
- Wang Huiyi Wu Guangqiang Power Flow Analysis and Control Design of a Vehicular Hydrostatic Energy Storage Transmission System SAE 952135
- Jiang Xiaoxia Research on the Secondary Regulation of Hydraulic Static drive and its Adaptive Control System Harbin Institute of Technology 1992
- Jiao Licheng The Application and Implementation of Neural Networks Xi'an Dian University Press 1993
- Wu Guangqiang Liu Qinghe Zhao Keding The intelligent control for the Hydrostatic Drive System of Secondary Regulation Using Neural Networks SAE 961838
- Sharpe Robert N. A Methodology Using Fuzzy Logic to Optimize Feedforward Artificial Neural Network Configurations IEEE Transactions on System, Man and Cybernetics 1994 24 5
- Mitra Sushmita Pal Sanker K. Self-Organizing Neural Network as a Fuzzy Classifier IEEE Transactions on System, Man and Cybernetics 1994 24 3