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采用多目标粒子群算法的本体元匹配方法
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作者 薛醒思 耿爱峰 BENINE Ramzi 《福建技术师范学院学报》 2022年第2期109-118,共10页
为了解决本体之间存在的异构问题,提出一种本体元匹配方法来确定不同本体中实体之间的对应关系.首先设计两个本体匹配结果质量的近似度量方法,并在此基础上构建本体匹配问题的多目标优化模型,最后提出一种多目标粒子群算法以求解该问题... 为了解决本体之间存在的异构问题,提出一种本体元匹配方法来确定不同本体中实体之间的对应关系.首先设计两个本体匹配结果质量的近似度量方法,并在此基础上构建本体匹配问题的多目标优化模型,最后提出一种多目标粒子群算法以求解该问题并优化本体匹配结果的质量.采用国际本体匹配竞赛提供的benchmark测试集,来测试基于多目标粒子群算法的本体元匹配方法的性能.实验结果表明:提出的方法在查全率和查准率两个指标上均优于国际本体匹配竞赛的其他参与者. 展开更多
关键词 本体元匹配 多目标粒子群算法 本体异构体 近似度量方法 国际本体匹配竞赛
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Optimization for ASP flooding based on adaptive rationalized Haar function approximation
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作者 Yulei Ge Shurong Li Xiaodong Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2018年第8期1758-1765,共8页
This paper presents an adaptive rationalized Haar function approximation method to obtain the optimal injection strategy for alkali-surfactant-polymer(ASP) flooding. In this process, the non-uniform control vector par... This paper presents an adaptive rationalized Haar function approximation method to obtain the optimal injection strategy for alkali-surfactant-polymer(ASP) flooding. In this process, the non-uniform control vector parameterization is introduced to convert original problem into a multistage optimization problem, in which a new normalized time variable is adopted on the combination of the subinterval length. Then the rationalized Haar function approximation method, in which an auxiliary function is introduced to dispose path constraints, is used to transform the multistage problem into a nonlinear programming. Furthermore, an adaptive strategy proposed on the basis of errors is adopted to regulate the order of Haar function vectors. Finally, the nonlinear programming for ASP flooding is solved by sequential quadratic programming. To illustrate the performance of proposed method,the experimental comparison method and control vector parameterization(CVP) method are introduced to optimize the original problem directly. By contrastive analysis of results, the accuracy and efficiency of proposed method are confirmed. 展开更多
关键词 Alkali-surfactant-polymer flooding OPTIMIZATION Enhanced oil recovery Mathematical modeling Rationalized Haar function approximation Adaptive strategy
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A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy
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作者 宫新保 臧小刚 周希朗 《Journal of Donghua University(English Edition)》 EI CAS 2005年第1期37-40,共4页
A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the str... A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances. 展开更多
关键词 radial basis function networks immune algorithm least square method.
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