| APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER |
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| 引用本文： | Yang Haiwei Zhan Yongqi Qiao Junwei Shi GuanglinSchool of Mechanical Engineering,Shanghai Jiaotong University,Shanghai 200030,China. APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER[J]. 机械工程学报(英文版), 2003, 16(3): 313-316 |
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| 作者姓名： | Yang Haiwei Zhan Yongqi Qiao Junwei Shi GuanglinSchool of Mechanical Engineering Shanghai Jiaotong University Shanghai 200030 China |
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| 作者单位： | Yang Haiwei Zhan Yongqi Qiao Junwei Shi GuanglinSchool of Mechanical Engineering，Shanghai Jiaotong University，Shanghai 200030，China |
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| 摘 要： | 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.
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| 关 键 词： | 神经网络 液压减震器 优化模型 参量优化 建筑学 |
APPLICATION OF ARCHITECTUREBASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER |
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| YangHaiwei ZhanYongqi QiaoJunwei. APPLICATION OF ARCHITECTUREBASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER[J]. Chinese Journal of Mechanical Engineering, 2003, 16(3): 313-316 |
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| Authors: | YangHaiwei ZhanYongqi QiaoJunwei |
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| Affiliation: | SchoolofMechanicalEngineering,ShanghaiJiaotongUniversity,Shanghai200030,China |
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| 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. |
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| Keywords: | Architecture-based Neural networks Modeling Parameter optimization Hydraulic bumper |
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