基于粒子群优化模糊核聚类的电梯群交通模式识别

Elevator traffic mode identification with kernel fuzzy clusteringbased on particle swarm optimization

摘要:

为了改善电梯群控系统的性能,使电梯群节能并高效运行,针对不同的交通模式采用合理的调度算法对电梯群进行优化调度,提出一种基于粒子群(PSO)的模糊核聚类算法(KFCM)的电梯交通流模式识别方法．利用基于梯度下降的粒子群优化算法代替KFCM算法的迭代过程,可使算法具有较强的全局搜索能力和局部搜索能力,并降低了KFCM算法对初始值的敏感度．利用核方法将低维特征空间的样本映射到高维特征空间,增加对样本特征的优化,并使样本特征在高维特征空间线性可分,更加容易聚类．采用在某办公楼采集的电梯交通流数据作为测试样本,仿真结果表明,与FCM聚类算法相比,该算法具有良好的性能指标,对电梯交通流的聚类效果更准确．

Abstract:

The elevator group is scheduled by suitable algorithm according to different traffic mode, and the performance of the elevator group control system will be improved. The kernel fuzzy clustering(KFCM) algorithm based on particle swarm optimization(PSO) is proposed to identify the elevator traffic mode. The iterative process based on gradient descent in KFCM algorithm is replaced by PSO algorithm, which has stronger global search capability and local search capability. Meanwhile the sensitivity to initial value of the FCM algorithm is decreased. By using kernel method, the sample in the low-dimensional feature space is mapped into high-dimensional feature space. And the sample feature is optimized and can be linearly divided in high-dimensional feature space so that clustering could be performed efficiently. The elevator traffic flow data collected from some office building is regard as the test sample. The simulation results show that the algorithm proposed has better performance indices compared with FCM algorithm, and the clustering effect of elevator traffic flow is more exact.