一种电梯交通流多模式预测方法的研究

Research on a Multi-mode Prediction Method for Elevator Traffic Flow

摘要: 在分析电梯交通流的基础上提出一种多模式预测方法．该方法首先利用人工免疫聚类算法（AI-CA）对电梯交通流进行离线的模式识别和分类，然后在此基础上利用高斯混合模型（GMM）对具有多种模式的电梯交通流进行数学建模．通过EM算法优化估计高斯混合模型的参数，得到了确定的高斯混合模型，从而实现对电梯交通流的在线预测．与其它预测方法的仿真结果进行了比较，体现出该方法的有效性和优越性．

Abstract: A multi-mode prediction method is proposed after analyzing the elevator traffic flow.Firstly,the elevator traffic flow is recognized and classified off-line into patterns through Artificial Immune Clustering Algorithm(AICA),and then Gaussian Mixture Model(GMM) is used to model the multi-mode elevator traffic flow.The EM algorithm is utilized to estimate the parameters of GMM to predict elevator traffic flow on line.The effectiveness of this method is shown by comparing simulation results with other prediction method.