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

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

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.

Pattern Recognition of Group Control Object Based on Fuzzy Neural Network

2010 ◽  
Vol 29-32 ◽  
pp. 2726-2732
Author(s):  
Huo You Li ◽  
Jian Jun Li ◽  
Jian Yang Li
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Object ◽  
Fuzzy Neural Networks ◽  
Traffic Model ◽  
Service Model ◽  
Object Based ◽  
Group Control ◽  
Fuzzy Neural ◽  
Elevator Group Control

This paper has proposed a concept of Group Control Object, taking an example according to experimental data of elevator group control object of a building; we apply fuzzy logic and neural network to recognize the pattern of the group control object. With the aid of the fuzzy neural network, this task designs to identify the different passenger flow, and classify it into the six models such as the up-peak service model, down-peak service, two way traffic model, four way traffic model, the balanced bi-story traffic model and free duty traffic model. Then it constructs five-level fuzzy neural networks to apply the classification to the elevator group control, and perform the best group control strategy for each model.

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