Pattern Recognition of Group Control Object Based on Fuzzy Neural Network

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.