The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of paralle...The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.展开更多
Timoshenko beam model is employed to investigate the vibration of atomic force mi- croscope （AFM） cantilevers in contact resonance force microscopy （CRFM）. Characteristic equation with both vertical and lateral ti...Timoshenko beam model is employed to investigate the vibration of atomic force mi- croscope （AFM） cantilevers in contact resonance force microscopy （CRFM）. Characteristic equation with both vertical and lateral tip-sample contact is derived. The contact resonance frequencies （CRFs） obtained by the Timoshenko model are compared with those by the Euler-Bernoulli model. A method is proposed to correct the wave number obtained by the Euler-Bernoulli model. The forced vibration is compared between the two models. Results reveal that the Timoshenko model is superior to the Euler-Bernoulli model in predicting the vibration characteristics for cantilevers＇ higher eigenmodes.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60873235&60473099)the Science-Technology Development Key Project of Jilin Province of China (No. 20080318)the Program of New Century Excellent Talents in University of China (No. NCET-06-0300).
文摘The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.
基金the financial support from National Natural Science Foundation of China under Grant nos.11502182 and 11402182the Fundamental Research Funds for the Central Universities (WUT:2015III025)
文摘Timoshenko beam model is employed to investigate the vibration of atomic force mi- croscope （AFM） cantilevers in contact resonance force microscopy （CRFM）. Characteristic equation with both vertical and lateral tip-sample contact is derived. The contact resonance frequencies （CRFs） obtained by the Timoshenko model are compared with those by the Euler-Bernoulli model. A method is proposed to correct the wave number obtained by the Euler-Bernoulli model. The forced vibration is compared between the two models. Results reveal that the Timoshenko model is superior to the Euler-Bernoulli model in predicting the vibration characteristics for cantilevers＇ higher eigenmodes.
