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support vector regression.model for complex target RCS predicting

Journal of Systems Engineering and Electronics 2009年第1期20卷 65-68页

作者： Wang Gu Chen Weishi Miao Jungang School of Electronic Engineering Beijing Univ. of Aeronautics and Astronautics Beijing 100083 P. R. China

The electromagnetic scattering computation has developed rapidly for many years; some computing problems for complex and coated targets cannot be solved by using the existing theory and computing models. A computing model based on data is established for making up the insufficiency of theoretic models. Based on the ＂support vector regression.method＂, which is formulated on the principle of minimizing a structural risk, a data model to predicate the unknown radar cross section of some appointed targets is given. Comparison between the actual data and the results of this predicting model based on support vector regression.method proved that the support vector regression.method is workable and with a comparative precision.

关键词： radar cross section complex target coated target support vector regression.

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FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID ECONOMETRIC-AI ENSEMBLE LEARNING APPROACH

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Journal of Systems Science & Complexity 2008年第1期21卷 1-19页

作者： Lean YU Shouyang WANG Kin Keung LAI Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China. Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong China.

Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression.(EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression.(SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

关键词： Artificial neural networks error-correction vector auto-regression foreign trade prediction hybrid ensemble learning kernel-based method support vector regression.

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Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

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Computers, Materials & Continua 2019年第3期58卷 727-760页

作者： Sifan Long Ming Zhao Xinfu He School of Software Central South UniversityChangsha410075China Laboratory of Irradiation Effect China Institute of Atomic EnergyBeijing102413China

With the development of society and the exhaustion of fossil energy,researcher need to identify new alternative energy *** energy is a very good choice,but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in ***,we studied the radiation performance of the fusion material reduced activation ferritic/martensitic(RAFM)*** main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method(GDM)which combines the gradient descent search strategy of the Convex Optimization Theory to get the best *** GDM algorithm to upgrade the annealing stabilization process of simulated annealing *** yield stress performance of RAFM steel is successfully predicted by the hybrid model which is combined by simulated annealing(SA)with support vector machine(SVM)as the first *** effect on yield stress by the main physical quantities such as irradiation temperature,irradiation dose and test temperature is also *** related prediction process is:first,we used the improved annealing algorithm to optimize the SVR model after training the SVR model on a training data ***,we established the yield stress prediction model of RAFM *** model can predict up to 96%of the data points with the prediction in the test set and the original data point in the 2*** statistical test analysis shows that under the condition of confidence level=0.01,the calculation results of the regression.effect significance analysis pass the T-test.

关键词： Convex optimization theory simulated annealing algorithm reduced activation ferritic/martensitic steel support vector regression.

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A New Fuzzy Identification Approach Using support vector regression.and Immune Clone Selection Algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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2009中国控制与决策会议

作者： WenJie Tian~1,Lan Ai~2,Yu Geng~3,JiCheng Liu~4 1.Automation Institute,BEIJING Union University,BEIJING,China,100101 2.Automation Institute,BEIJING Union University,BEIJING,China,100101 3.Automation Institute,BEIJING Union University,BEIJING,China,100101 4.Automation Institute,BEIJING Union University,BEIJING,China,100101

＜正＞A new fuzzy identification approach using support vector regression.SVR） and immune clone selection algorithm（ICSA） is presented in this *** positive definite reference function is utilized to construct a qualified Mercer kernel for *** an improved ICSA is developed for parameters selection of SVR,in which the number of support vectors and regression.accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy ***,a set of TS fuzzy rules can be extracted from the SVR *** results show that the resulting fuzzy model not only costs less fuzzy rules,but also possesses good generalization ability.

关键词： Immune clone selection algorithm Fuzzy system identification Positive defmite reference function TS fuzzy rule support vector regression.

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