Traffic mode recognition method based on genetic algorithm and fuzzy neural network

Abstract

The invention belongs to the technical field of traffic, particularly relates to a traffic mode recognition method based on a genetic algorithm and a fuzzy neural network, and aims to recognize the traffic mode of an elevator, perform preprocessing and label construction on training data by using a k-means clustering method, and improve the traffic mode recognition accuracy. And constructing a three-layer fuzzy neural network model to output the prediction probability of each traffic mode, and initializing a weight coefficient in the constructed fuzzy neural network model by using a genetic algorithm. According to the method, the neural network is effectively prevented from falling into a local optimal solution when the target is optimized, and the program performance in the traffic mode recognition process is improved. The weight of the neural network is initialized by using a genetic algorithm, and a basis can be provided for subsequent neural network back propagation optimization. By adopting the fuzzy logic method, the situation of overfitting of the neural network can be reduced, and the process of rapid change of the passenger flow volume under a certain special condition is smoothed, so that the training of the neural network and the prediction of the traffic mode are more accurate.

Description

technical field [0001] The invention relates to a traffic pattern recognition method, in particular to a traffic pattern recognition method based on a genetic algorithm and a fuzzy neural network. Background technique [0002] With the continuous development of the economy and society and the gradual improvement of the level of computer communication technology, elevators have gradually become the main means of transportation for people in various high-rise buildings, and the scale and demand of the elevator industry are increasing day by day. In 2020, the annual new sales of elevators in the world will reach about 1.5 million units, and the growth rate of my country's elevator industry will remain at about 10%. The elevator production and manufacturing industry will also become one of the pillar industries in some cities. Elevator scheduling technology and elevator group control system are the core and brain of elevator operation, and are also important research directions ...

AI Technical Summary

Problems solved by technology

For machine learning methods, although the above process can be improved, it cannot process and describe fuzzy information, and cannot effectively use existing empirical knowledge. At the same time, it has high requirements for the format, accuracy, and units of feature data. The processing of recognition sets up obstacles

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