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HYPERSTATIC STRUCTURE MAPPING MODEL BUILDING AND OPTIMIZING DESIGN 被引量:2
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作者 XU Gening GAO Youshan +1 位作者 ZHANG Xueliang YANG Ruigang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第1期55-59,共5页
Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method (FEM) analysis commerce software. M... Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method (FEM) analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output dam. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrpolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system (SSHLPS) is calculated as an instance. A three-layer back-propagation (BP) net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrpulation contrasts with integrity re-analysis. Any layer SSHLPS among 1-8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is appfied into the same topological structure, with reduced distortion and assured precision. 展开更多
关键词 Plane model - Space model Artificial neural networks Mapping model Optimum design
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