针对有阵列孔径阵元总数和最小阵元间距约束的稀布同心圆环阵列综合问题,提出了一种基于修正遗传算法(Modified real Genetic Algorithm,MGA)的半径优化方法.通过约束同一圆环上阵元间距相等,利用MGA优化圆环的半径,获得最小的峰值旁瓣...针对有阵列孔径阵元总数和最小阵元间距约束的稀布同心圆环阵列综合问题,提出了一种基于修正遗传算法(Modified real Genetic Algorithm,MGA)的半径优化方法.通过约束同一圆环上阵元间距相等,利用MGA优化圆环的半径,获得最小的峰值旁瓣电平.该方法不仅降低了优化的计算量和模型的复杂性,而且还有效地改善了阵列的旁瓣性能.仿真结果证明了该方法的有效性和鲁棒性.展开更多
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
文摘针对有阵列孔径阵元总数和最小阵元间距约束的稀布同心圆环阵列综合问题,提出了一种基于修正遗传算法(Modified real Genetic Algorithm,MGA)的半径优化方法.通过约束同一圆环上阵元间距相等,利用MGA优化圆环的半径,获得最小的峰值旁瓣电平.该方法不仅降低了优化的计算量和模型的复杂性,而且还有效地改善了阵列的旁瓣性能.仿真结果证明了该方法的有效性和鲁棒性.
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.
