计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2014年
z1期
159-161,172
,共4页
分数阶PID%萤火虫算法%遗传算法%混合粒子群算法%混合计算智能算法
分數階PID%螢火蟲算法%遺傳算法%混閤粒子群算法%混閤計算智能算法
분수계PID%형화충산법%유전산법%혼합입자군산법%혼합계산지능산법
fractional Proportion-Integral-Derivative ( PID)%Glowworm Swarm Optimization ( GSO)%Genetic Algorithm (GA)%hybrid particle swarm optimization algorithm%hybrid computation intelligent learning algorithm
为解决分数阶PID控制器五维参数优化的难题，设计了一种把萤火虫算法和遗传算法相结合的混合计算智能算法，阐述了计算智能中的群智能算法和进化计算的基本原理和数学算法。该方法基于生物的群体智能和个体进化相结合的思想，能够有效地提高寻优精度，并使算法向最优方向不断进化。经过仿真验证，混合算法在分数阶PID参数整定方面具有运算时间短、仿真精度高等优点。
為解決分數階PID控製器五維參數優化的難題，設計瞭一種把螢火蟲算法和遺傳算法相結閤的混閤計算智能算法，闡述瞭計算智能中的群智能算法和進化計算的基本原理和數學算法。該方法基于生物的群體智能和箇體進化相結閤的思想，能夠有效地提高尋優精度，併使算法嚮最優方嚮不斷進化。經過倣真驗證，混閤算法在分數階PID參數整定方麵具有運算時間短、倣真精度高等優點。
위해결분수계PID공제기오유삼수우화적난제，설계료일충파형화충산법화유전산법상결합적혼합계산지능산법，천술료계산지능중적군지능산법화진화계산적기본원리화수학산법。해방법기우생물적군체지능화개체진화상결합적사상，능구유효지제고심우정도，병사산법향최우방향불단진화。경과방진험증，혼합산법재분수계PID삼수정정방면구유운산시간단、방진정도고등우점。
In order to solve the challenging problem of optimization of five-dimensional parameters in fractional PID controller, based on the introduction of swarm intelligence algorithm and evolutionary computing, a hybrid computation intelligent learning algorithm was proposed, which combined Glowworm Swarm Optimization ( GSO) with Genetic Algorithm ( GA) . The hybrid algorithm was based on the swarm intelligence and individual evolution of creatures, which can greatly increase the accuracy of optimization and ensure that algorithm evolves to optimum. A series of experiments verify that the proposed hybrid algorithm can shorten the time of computing and increase the accuracy of simulation.
