For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
The analysis method of the triple test cross design has been discussed carefully from the two factor experiment design and the genetic models of additive dominant effect and of epistasis effect.Two points different f...The analysis method of the triple test cross design has been discussed carefully from the two factor experiment design and the genetic models of additive dominant effect and of epistasis effect.Two points different from the previous reports have been concluded: (1)both the degrees of freedom of the orthogonal terms C2 and C3 are m, (2)the denominator in the F test to C2 and C3 is the error mean of square between plots.展开更多
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
文摘The analysis method of the triple test cross design has been discussed carefully from the two factor experiment design and the genetic models of additive dominant effect and of epistasis effect.Two points different from the previous reports have been concluded: (1)both the degrees of freedom of the orthogonal terms C2 and C3 are m, (2)the denominator in the F test to C2 and C3 is the error mean of square between plots.
