摘要
提出一种新的智能优化调度方法,将再励学习控制运用到电梯群控系统中,采用基于交通模式识别的小脑模型神经网络作为控制器,以乘客平均候梯时间最短为控制目标设计出电梯群控系统的控制方案.该控制方法不需要过多的专家知识及学习样本,可以实现在线学习并具有较强的自适应能力,提高了系统的效率并且使系统性能得到优化.以层间交通模式为例对系统进行仿真,结果证明了该方法的可行性及有效性.
A new intelligent optimized dispatching method is proposed, and reinforcement learning control is applied in elevator group control system, in which CMAC neural network based on traffic pattern recognition is designed as the controller, in order to optimize the passengers＇ average waiting time. This method can train weights in neural network on-line, not only without many expert knowledge and learning samples, but also with stronger adaptive ability. As a result, the system efficiency is improved, and the system performance is optimized. The simulation is performed under the pattern of interbedded traffic, and the results show that the method is feasible and effective.
出处
《信息与控制》
CSCD
北大核心
2005年第4期495-499,共5页
Information and Control
基金
国家自然科学基金资助项目(60474042)
关键词
电梯群控
模式识别
再励学习控制
小脑模型神经网络
elevator group control
pattern recognition
reinforcement learning control
CMAC neural network
作者简介
刘建昌（1960-），男，博士，教授。研究领域为智能控制理论与应用，复杂过程控制技术。
林琳（1979-），女，硕士。研究领域为智能控制理论与应用。
