计算机应用研究
計算機應用研究
계산궤응용연구
APPLICATION RESEARCH OF COMPUTERS
2014年
11期
3266-3268,3272
,共4页
张凌晓%刘克成%杨新锋%张军朝
張凌曉%劉剋成%楊新鋒%張軍朝
장릉효%류극성%양신봉%장군조
粒子滤波%优化算法%多维函数%非线性约束优化
粒子濾波%優化算法%多維函數%非線性約束優化
입자려파%우화산법%다유함수%비선성약속우화
particle filter%optimization algorithm%multi-dimensional function%nonlinear constraint optimization
传统的非线性约束优化算法的精度较低,为了克服这一问题,提出了一种基于粒子滤波的新型优化算法。该算法用于解决非线性约束优化问题,并结合粒子滤波器的模型和机制。首先,利用粒子滤波算法的基本原理建立这种优化算法,并给出算法的操作步骤;然后将非线性约束优化问题转换为函数优化问题函数优化问题,并针对非线性约束优化问题,建立粒子滤波优化算法的数学模型。仿真实验结果证明了这种新型算法的正确性,并且表明了相对于传统的优化算法,基于粒子滤波器的优化方法在解决非线性优化问题方面具有更高的效率和速率,并对今后的非线性约束优化问题具有适应性。
傳統的非線性約束優化算法的精度較低,為瞭剋服這一問題,提齣瞭一種基于粒子濾波的新型優化算法。該算法用于解決非線性約束優化問題,併結閤粒子濾波器的模型和機製。首先,利用粒子濾波算法的基本原理建立這種優化算法,併給齣算法的操作步驟;然後將非線性約束優化問題轉換為函數優化問題函數優化問題,併針對非線性約束優化問題,建立粒子濾波優化算法的數學模型。倣真實驗結果證明瞭這種新型算法的正確性,併且錶明瞭相對于傳統的優化算法,基于粒子濾波器的優化方法在解決非線性優化問題方麵具有更高的效率和速率,併對今後的非線性約束優化問題具有適應性。
전통적비선성약속우화산법적정도교저,위료극복저일문제,제출료일충기우입자려파적신형우화산법。해산법용우해결비선성약속우화문제,병결합입자려파기적모형화궤제。수선,이용입자려파산법적기본원리건립저충우화산법,병급출산법적조작보취;연후장비선성약속우화문제전환위함수우화문제함수우화문제,병침대비선성약속우화문제,건립입자려파우화산법적수학모형。방진실험결과증명료저충신형산법적정학성,병차표명료상대우전통적우화산법,기우입자려파기적우화방법재해결비선성우화문제방면구유경고적효솔화속솔,병대금후적비선성약속우화문제구유괄응성。
In order to overcome the problem of low solution precision of traditional nonlinear constraint algorithms,this paper-proposed a new optimization algorithm based on particle filter.It used this algorithm to solve nonlinear constraint problem,and combined the model and mechanism of particle filters.It established the optimization algorithm based on particle filter algo-rithm,and gave the procedures of which as well.Then it converted the nonlinear constraint optimization problems to function optimization problems,and established mathematical models of particle filter optimization algorithm for the nonlinear constraint optimization problems.Simulation results verify the validity of the new algorithm,and show that the optimization method based on particle filter has higher efficiency and rate than the traditional optimization algorithm when solving nonlinear optimization problems.Meanwhile,the proposed algorithm has its feasibility to future research on nonlinear constraint optimization prob-lems.