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Yang Haiwei
Zhan Yongqi
Qiao Junwei
Shi Guanglin
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200030, China |
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APPLICATION OF ARCHITECTURE
BASED NEURAL NETWORKS IN
MODELING AND PARAMETER
OPTIMIZATION OF HYDRAULIC
BUMPER
Abstract: The dynamic working process
of 52SFZ-140-207B type of hydraulic bumper is analyzed. The
modeling method using architecture-based neural networks is
introduced. Using this modeling method, the dynamic model of the hydraulic bumper is established; Based on this model the structural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result shows that the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamic performance of the hydraulic bumper is improved through parameter optimization.
Key words:
Architecture-based Neural networks Modeling Parameter optimization
Hydraulic bumper
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