多维关联函数建模及其低碳实例检索应用

摘要/Abstract

摘要： 针对低碳设计多维度实例检索建模及其降维计算问题，提出了一种多维关联函数建模及其检索应用方法。首先，通过变斜率原理改进侧距，构建了基于改进侧距的多维侧距模型。然后，针对侧距难以准确描述设计参数与最优值之间距离的问题，建立了多维关联函数及其三层次的低碳实例检索方法。最后以螺杆空压机为例进行了验证，结果表明了改进侧距的正确性和有效性、多维关联函数降维计算的快速性与有效性，以及低碳实例检索的合理性与实例间的差异性。

关键词: 多维关联函数, 低碳实例, 相似检索, 空间降维, 螺杆空气压缩机

Abstract: To solve problems of multidimensional case retrieval modeling and its dimensionality reduction calculation for low carbon design, a modeling and application method of multi-dimensional correlation function was proposed. Firstly, an improved side distance method was developed through the principles of variable slope, which provided the basis for developing a multi-dimensional side distance model. Secondly, because side distance formula could not give out the accurate distance values between design parameters and its optimal, a multi-dimensional correlation function which had good dimension reduction effect was established. Then a retrieval method was given based on the multi-dimensional correlation function. Finally, the proposed method and algorithms were verified in the design of a screw air compressor. The evaluation results show that the improved side distance is correct and effective, the dimension reduction method for the multi-dimensional correlation function is fast and viable, and the otherness among these cases is very obvious.

Key words: multi-dimensional correlation function, low-carbon case, similarity retrieval, dimension reduction, screw air compressor

中图分类号:

TP18

洪欢欢, 赵燕伟, 陈尉刚. 多维关联函数建模及其低碳实例检索应用[J]. 中国机械工程.

HONG Huanhuan, ZHAO Yanwei, CHEN Weigang. Modeling of Multi-dimensional Correlation Function and Its Applications for Low Carbon Case Retrieval[J]. China Mechanical Engineering.

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