Adaptive Optimal Elevator Group Control by Use of Neural Networks
Adaptive Optimal Elevator Group Control by Use of Neural Networks
复制标题
利用神经网络的自适应最优电梯群控制
DOI：
10.5687/iscie.7.487
复制
发表时间：
1994
期刊：
影响因子：
--
通讯作者：
Y. Nishikawa
中科院分区：
文献类型：
--
作者：
S. Markon;H. Kita;Y. Nishikawa
一组电梯的控制是一个困难的随机控制问题，因为随机且无法预测的乘客到达。在这里，我们提出了一种通过使用神经网络和基于随机近似的在线增强学习的组合来构造随机系统自适应控制器的新方法。这种方法将有效的监督学习与一般的强化适应相结合。监督学习用于用特定领域的知识准备控制器，并将其初始化以模拟现有控制器。增强学习用于适应，并特别注意允许在线操作。新方法用于开发自适应，最佳的电梯组控制器。仿真测试的结果表明，将建议的方法用于工业应用。
The control of a group of elevators is a difficult stochastic control problem, because of the random and unpredictable passenger arrivals. Here we propose a new method for constructing an adaptive controller for stochastic systems, by using a combination of neural networks and an on-line reinforcement learning based on stochastic approximation. This method combines an efficient supervised learning with a general reinforcement adaptation. The supervised learning is used to prepare the controller with domain-specific knowledge, and for initializing it to emulate an existing controller. The reinforcement learning is used for adaptation, with a special attention paid to allow online operation. The new method is used to develop an adaptive, optimal elevator group controller. Results of simulation tests indicate the feasibility of using the proposed method for industrial applications.