The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

Article Preview

Abstract:

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

653-657

Citation:

Online since:

February 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Luo Fei, Xu Yuge, Cao Jianzhong. Elevator traffic flow prediction with least squares suppprt vector machines. Proceedings of the fourth international conference on machine learning and cybernetics. 2005: 4266-4270.

DOI: 10.1109/icmlc.2005.1527686

Google Scholar

[2] Robert H. Crites, Andrew G. Barto. Elevator Group Control Using Multiple Reinforcement Learning Agents[J] Machine Learning, 1998, 33, (2-3).

Google Scholar

[3] Yang Zhenshan, Shao Cheng. On the present situation and developing trends of elevator group supervisory control technologies[J]. 2005, 20(12).

Google Scholar

[4] Luo Fei, Zhao Xiaocui. New multi-objective optimal scheduling strategy for EGCS. Automation& Instrumentation. 2010, 25(9).

Google Scholar

[5] Lei DeMing; Yan XinPing. Intelligent Multi-objective optimization algorithm and application[M]. Beijing, Science Press. (2009).

Google Scholar

[6] Li Tianjian. The Design of Dispatching Method for Elevator Group Control Based on the Multi-object Optimization[J]. Science Technology and engineeting. 2010, 10(28).

Google Scholar