笔者介绍了无人直升机航磁系统的研发与集成,采用无人直升机作为飞行平台,搭载高精度航空磁测系统,具有低成本、高效率、不受机场跑道限制、可夜航、按设计测线全自主导航飞行等特点。该系统分别完成了磁补偿试验飞行与实际应用工作,补...笔者介绍了无人直升机航磁系统的研发与集成,采用无人直升机作为飞行平台,搭载高精度航空磁测系统,具有低成本、高效率、不受机场跑道限制、可夜航、按设计测线全自主导航飞行等特点。该系统分别完成了磁补偿试验飞行与实际应用工作,补偿精度达到0.046 9 n T,测量成果与测区内以往航磁成果对比,其反映的地磁场特征形态基本一致,验证了该系统的有效性,航磁异常等值线在细节上表现更细致。本系统为大比例尺、高精度、小面积的航磁测量工作提供了一种高效灵活的工作手段。展开更多
Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) ...Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem,a generalized version of the satisfiability (SAT) problem.The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm,competing with other popular algorithms such as simulated annealing and WALKSAT.Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.展开更多
The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging re...The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning.In this study,we present a novel method for simultaneous optimization of the SVR kernel function and its parameters,formulated as a mixed integer optimization problem and solved using the recently proposed heuristic 'extremal optimization (EO)'.We present the problem formulation for the optimization of the SVR kernel and parameters,the EO-SVR algorithm,and experimental tests with five benchmark regression problems.The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
文摘笔者介绍了无人直升机航磁系统的研发与集成,采用无人直升机作为飞行平台,搭载高精度航空磁测系统,具有低成本、高效率、不受机场跑道限制、可夜航、按设计测线全自主导航飞行等特点。该系统分别完成了磁补偿试验飞行与实际应用工作,补偿精度达到0.046 9 n T,测量成果与测区内以往航磁成果对比,其反映的地磁场特征形态基本一致,验证了该系统的有效性,航磁异常等值线在细节上表现更细致。本系统为大比例尺、高精度、小面积的航磁测量工作提供了一种高效灵活的工作手段。
基金supported by the National Natural Science Foundation of China (No.61074045)the National Basic Research Program (973) of China (No.2007CB714000)the National Creative Research Groups Science Foundation of China (No.60721062)
文摘Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem,a generalized version of the satisfiability (SAT) problem.The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm,competing with other popular algorithms such as simulated annealing and WALKSAT.Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.
文摘The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters.The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning.In this study,we present a novel method for simultaneous optimization of the SVR kernel function and its parameters,formulated as a mixed integer optimization problem and solved using the recently proposed heuristic 'extremal optimization (EO)'.We present the problem formulation for the optimization of the SVR kernel and parameters,the EO-SVR algorithm,and experimental tests with five benchmark regression problems.The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
