基于FCM聚类模糊神经网络的电梯交通模式识别

doi:10.16182/j.issn1004731x.joss.201804027

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (4): 1433-1439.doi: 10.16182/j.issn1004731x.joss.201804027

基于FCM聚类模糊神经网络的电梯交通模式识别

杨祯山¹, 岳文姣²

1.渤海大学工学院,辽宁锦州 121013;
2.亚太建设科技信息研究院有限公司,北京 100120

收稿日期:2016-05-13 修回日期:2016-10-06 出版日期:2018-04-08 发布日期:2019-01-04
作者简介:杨祯山(1965-),男,辽宁锦州,博士,教授,研究方向为高层建筑垂直交通系统的配置与优化调度,先进控制技术在智能楼宇中的应用;岳文姣(1990-),女,河北保定,硕士,研究方向为高层建筑电梯垂直交通的配置与控制技术。
基金资助:
国家自然科学基金(60874026)

Elevator Traffic Pattern Recognition with FCM Clustering Based Fuzzy Neural Network

Yang Zhenshan¹, Yue Wenjiao²

1.College of Engineering, Bohai University, Jinzhou 121013, China;
2.Asia-Pacific Institute of Construction SciTech Information Co.,Ltd., Beijing 100120, China

Received:2016-05-13 Revised:2016-10-06 Online:2018-04-08 Published:2019-01-04

摘要/Abstract

摘要： 电梯交通模式识别是有效实施电梯群控策略的前提。针对电梯交通的时变、非线性及不确定性的特点,提出一种基于FCM (Fuzzy C-means)聚类模糊神经网络的电梯交通模式识别方法。该方法将模糊逻辑技术用于神经网络计算和学习,通过FCM对原始交通需求的聚类,实现输入空间的模糊划分,确定网络隶属函数的参数初始值及聚类中心并获取模糊规则,提高神经网络学习能力,使隶属函数加权系数根据不同的交通模式改变。利用神经网络完成并行模糊推理,实现电梯交通模式的识别。仿真试验表明了该方法的有效性。

关键词: 电梯交通需求, 模式识别, 模糊神经网络, FCM聚类算法, 专家经验

Abstract: Elevator traffic demand pattern recognition is the prerequisite for effectively implementing the strategies of elevator group control system. In view of the characteristics of time-varying, nonlinear and uncertainty of elevator traffic demand, an elevator traffic pattern recognition method with FCM (Fuzzy C-means) clustering based fuzzy neural network is presented. The method introduces the fuzzy logic into the calculation and learning of BP neural network, and employs FCM clustering algorithm to cluster the original traffic demand to realize the fuzzy partition of the input space of fuzzy system to determine the initial value of network membership function and clustering center and to obtain the fuzzy rules, which improves the learning ability of neural network and makes the weighted coefficients of the membership function vary with different traffic patterns. The elevator traffic pattern is recognized by the parallel fuzzy reasoning of neural network. Simulation experiments show the validity of the presented method.

Key words: elevator traffic demand, pattern recognition, fuzzy neural network, FCM clustering algorithm, expert experience

中图分类号:

杨祯山, 岳文姣. 基于FCM聚类模糊神经网络的电梯交通模式识别[J]. 系统仿真学报, 2018, 30(4): 1433-1439.

Yang Zhenshan, Yue Wenjiao. Elevator Traffic Pattern Recognition with FCM Clustering Based Fuzzy Neural Network[J]. Journal of System Simulation, 2018, 30(4): 1433-1439.

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基于FCM聚类模糊神经网络的电梯交通模式识别

Elevator Traffic Pattern Recognition with FCM Clustering Based Fuzzy Neural Network

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