WNN Short-Term Traffic Flow Prediction Based on Improved Mind Evolutionary Algorithm and Error Compensation

In the research and design of intelligent traffic system, urban road traffic control and guidance is an important research topic, and short-term traffic flow prediction is also an important research content of urban road traffic control and guidance. The combined prediction model is the research trend of short-term traffic flow prediction model in recent years. Nowadays, there is a prediction model of mind evolutionary algorithm to optimize wavelet neural network (MEA-WNN). The convergence and alienation of mind evolutionary algorithm are too random, and the bulletin board information is not supplemented. This paper introduces the particle movement update position method after convergence, which is similar to particle swarm optimization(PSO). Thus, WNN prediction model based on improved mind evolution algorithm (IMEA-WNN) is constructed. In order to improve the accuracy of the prediction model, the error compensation method is introduced to construct the combined prediction model (IMEA-EC-WNN). In this paper, the simulation results of IMEA-EC-WNN model are compared with other prediction models. The prediction effect of IMEA-EC-WNN model is better, and it has practical application value.