计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
31期
241-244
,共4页
微粒群优化算法%模拟退火%神经网络%软测量
微粒群優化算法%模擬退火%神經網絡%軟測量
미립군우화산법%모의퇴화%신경망락%연측량
Particle Swarm Optimization (PSO) algorithm%Simulated Annealing(SA)%Neural Network(NN)%soft-sensor
综合基本微粒群优化算法(Particle Swarm Optimization,PSO)和模拟退火(Simulated Annealing,SA)算法,提出了一种新型的协同进化方法(SAPSO).通过PSO和SA两种算法的协同搜索,可以有效地克服微粒群算法的早熟收敛.用SAPSO训练神经网络,并将其用于延迟焦化装置粗汽油干点和高压聚乙烯熔融指数的软测量建模.与几种常见建模方法比较,结果表明该软测量模型具有更高的测量精度和更好的泛化性能,能够满足现场测量要求.
綜閤基本微粒群優化算法(Particle Swarm Optimization,PSO)和模擬退火(Simulated Annealing,SA)算法,提齣瞭一種新型的協同進化方法(SAPSO).通過PSO和SA兩種算法的協同搜索,可以有效地剋服微粒群算法的早熟收斂.用SAPSO訓練神經網絡,併將其用于延遲焦化裝置粗汽油榦點和高壓聚乙烯鎔融指數的軟測量建模.與幾種常見建模方法比較,結果錶明該軟測量模型具有更高的測量精度和更好的汎化性能,能夠滿足現場測量要求.
종합기본미립군우화산법(Particle Swarm Optimization,PSO)화모의퇴화(Simulated Annealing,SA)산법,제출료일충신형적협동진화방법(SAPSO).통과PSO화SA량충산법적협동수색,가이유효지극복미립군산법적조숙수렴.용SAPSO훈련신경망락,병장기용우연지초화장치조기유간점화고압취을희용융지수적연측량건모.여궤충상견건모방법비교,결과표명해연측량모형구유경고적측량정도화경호적범화성능,능구만족현장측량요구.
A novel cooperative evolutionary algorithm (SAPSO) is proposed by taking advantage of both Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm.It can validly overcome the premature problem in PSO through cooperative search between PSO and SA.Then, SAPSO is employed to train artificial neural network and applied to soft-sensing of gasoline endpoint of delayed coking plant and melt-index of High Pressure Low-density Polyethylene yield.Its performance is compared with existing soft sensor modeling methods.The simulation results show that this model has higher measuring precision as well as better generalization ability,and can satisfy the need of spot measurement.
