A traffic pattern recognition method of elevator group control systems based on fuzzy neural networks is presented in this paper.
介绍采用模糊神经网络进行电梯群控系统交通模式识别的方法。
The thesis analyses the characters of elevator traffic system firstly, further point out the identifying of traffic pattern by the statistics about the elevator traffic.
本文首先对电梯交通模式的类别、特点进行了分析，提出了基于电梯客流统计特性的客流交通模式识别方法。
Traffic pattern recognition method of elevator group control systems is presented. The method consists of two steps, in which fuzzy neural networks are employed.
介绍了应用两个模糊神经网络分两步进行电梯群控系统交通模式识别，提出了利用专家知识获取样本和训练网络的步骤。
The structure of the FNN is given in the paper, which is the basic theory for the traffic pattern recognition and the elevator assignment.
针对控制目标对派梯原则进行详细的研究，并且派梯原则是以当前的交通模式为前提的，应用了交通模式识别的结果。
Aiming at the pattern recognition of traffic flows in elevator group control systems, a method based on the multi-value classification SVM (Support Vector Machine) is put forward.
针对电梯群控调度中的交通流模式识别问题，提出了一种基于多值分类支持向量机的电梯交通流模式识别方法。
Aiming at the pattern recognition of traffic flows in elevator group control systems, a method based on the multi-value classification SVM (Support Vector Machine) is put forward.
针对电梯群控调度中的交通流模式识别问题，提出了一种基于多值分类支持向量机的电梯交通流模式识别方法。
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