| 协同进化算法及在软测量建模中的应用 |
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| 引用本文： | 卜艳萍,俞金寿.协同进化算法及在软测量建模中的应用[J].计算机工程与应用,2009,45(31):241-244. |
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| 作者姓名： | 卜艳萍 俞金寿 |
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| 作者单位： | 1.华东理工大学 自动化研究所，上海 200237 ;2.上海交通大学 技术学院，上海 201101 |
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| 摘 要： | 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。
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| 关 键 词： | 微粒群优化算法 模拟退火 神经网络 软测量 |
| 收稿时间： | 2008-6-16 |
| 修稿时间： | 2008-10-8
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Cooperative evolutionary algorithm and its application in soft sensor modeling |
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| BU Yan-ping,YU Jin-shou.Cooperative evolutionary algorithm and its application in soft sensor modeling[J].Computer Engineering and Applications,2009,45(31):241-244. |
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| Authors: | BU Yan-ping YU Jin-shou |
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| Affiliation: | 1.Research Institute of Automation，East China University of Science and Technology，Shanghai 200237，China 2.School of Technology，Shanghai Jiaotong University，Shanghai 201101，China |
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| Abstract: | 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. |
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| Keywords: | Particle Swarm Optimization (PSO) algorithm Simulated Annealing(SA) Neural Network(NN) soft-sensor |
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