Ant colony algorithm and fuzzy neural network-based intelligent dispatching algorithm of an elevator group control system

被引：0

作者：

Liu, Jianchang ^{[1
]}

Liu, Yiyang ^{[2
,3
]}

机构：

[1] Northeastern Univ, Minist Educ, Key Lab Integrated Automat Proc Ind, Shenyang, Peoples R China

[2] Chinese Acad Sci, Shenyang Inst Automat, Robot Lab, Shenyang, Peoples R China

[3] Chinese Acad Sci, Grad Sch, Shenyang, Peoples R China

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年

基金：

国家教育部博士点专项基金资助;

关键词：

elevator group control; ant colony algorithm; fuzzy neural network; intelligent dispatching;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To improve the performance of elevator group control systems (EGCS), an intelligent dispatching method based on ant colony algorithm and fuzzy neural network is presented. An elevator group control system based on fuzzy neural network adapts to various traffic flow modes. Using ant colony algorithm to optimize the weights of fuzzy neural network before training with BP algorithm can solve the problem that convergence of weights is easy to be trapped in local optimal values when trained just with BP algorithm. This intelligent dispatching algorithm makes the weights of fuzzy neural network more precise and reasonable. These weights greatly affect the performance of an EGCS. The results of simulation show that ant colony algorithm and fuzzy neural network greatly improves the performance of an EGCS. Its average waiting time is obviously shorter than that of the EGCS that is only based on fuzzy neural network.

引用

页码：414 / +

页数：2

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