Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive cal...Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.展开更多
This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework base...This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.展开更多
For the purpose of describing the deformation characteristics of rocks,the effect of volume changes on mechanical properties of rocks should be taken into account with relation to the development of constitutive model...For the purpose of describing the deformation characteristics of rocks,the effect of volume changes on mechanical properties of rocks should be taken into account with relation to the development of constitutive model.Firstly,rocks are divided into three parts,i.e.,voids,a damaged part and an undamaged part in the course of loading.The void ratio was applied to describing the changes of voids or pores during the deformation process.Then,using statistical damage theory,a constitutive model was developed for rocks to describe their strain softening and hardening on the basis of investigating the relationship between the net stress and apparent stress,in which the influence of volume changes on rock behavior was correctly taken into account,such as the initial phase of compaction and the latter stage of dilation.Thirdly,a method of determining model parameters was also presented.Finally,this model was used to compare the theoretical results with those observed from experiments under conventional triaxial loading conditions.展开更多
The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then t...The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.展开更多
基金Project (SGKJ[200301-16]) supported by the State Grid Cooperation of China
文摘Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.
文摘This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.
基金Project(2006AA11Z104) supported by the National High-Tech Research and Development Program of China
文摘For the purpose of describing the deformation characteristics of rocks,the effect of volume changes on mechanical properties of rocks should be taken into account with relation to the development of constitutive model.Firstly,rocks are divided into three parts,i.e.,voids,a damaged part and an undamaged part in the course of loading.The void ratio was applied to describing the changes of voids or pores during the deformation process.Then,using statistical damage theory,a constitutive model was developed for rocks to describe their strain softening and hardening on the basis of investigating the relationship between the net stress and apparent stress,in which the influence of volume changes on rock behavior was correctly taken into account,such as the initial phase of compaction and the latter stage of dilation.Thirdly,a method of determining model parameters was also presented.Finally,this model was used to compare the theoretical results with those observed from experiments under conventional triaxial loading conditions.
基金the National Natural Science Foundation of China (60503024, 50634010)
文摘The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.
