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《Journal of Mechanical Strength》 2005-04
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HU Qiao~(1) HE ZhengJia~(2) ZI YanYang~(1) LEI YaGuo~(1) LIU JingKe~(1) (1.Department of Mechanical Engineering,Xi′an Jiaotong University,Xi′an 710049,China) (2.State Key Laboratory for Manufacturing System Engineering,Xi′an 710049,China)  
Due to the fluctuation and complexity of electromechanical equipment operation condition affected by various factors, it is difficult to use a single prediction method to accurately describe its moving trend. So a new hybrid prediction model based on improved grey system, support vector machine (SVM) and neuro-fuzzy system is proposed. In this model, the fluctuation of the data sequence is weakened by the improved grey system, the SVM can deal with small samples and neuro-fuzzy system is capable of processing non-linear fuzzy information. The hybrid prediction model combines these advantages and its prediction result is an adaptive combination of these single method's via improved genetic algorithms. This model was applied to the trend prediction of a fluctuant and complicated benchmark data and a vibration trend signal from machine sets. Testing results show that the prediction performance of this model outperforms any one of the three prediction methods.
【Fund】: 国家自然科学基金重点资助项目(50335030 50175087 50305012);; 国家重点基础研究发展计划(973计划)(2005CB724106);; 高校博士点基金资助项目(20040698026);; 西安交通大学科学研究基金资助项目~~
【CateGory Index】: TH17
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