Chaotic search optimization method for traffic flow prediction of adaptive neural network

Abstract

The invention provides a chaotic search optimization method for traffic flow prediction of an adaptive neural network. The method comprises the following steps: S1, constructing a BP neural network model, and initializing network parameters; s2, initializing various parameters of a sparrow algorithm; s3, adding a Tent chaotic mapping initialization population; s4, calculating the fitness value of the sparrows in the population; s5, sorting the populations according to the fitness values; s6, updating the position of the producer; s7, updating the position of the follower; s8, updating the position of the sparrow in danger; s9, updating the optimal fitness value of the individual, then updating the optimal fitness value of the group, and entering the step S10; s10, judging whether the number of iterations is reached or not, and if not, returning to the step S5; otherwise, outputting the optimal fitness value and the global optimal position, and entering the step S11; and S11, endowing the optimal fitness value and the global optimal position to the BP neural network model, optimizing the weight and the threshold value of the BP neural network model, and performing prediction to complete the construction of the CSSA-BP model. The method is higher in prediction accuracy and higher in iteration speed.

Description

【Technical field】 [0001] The invention relates to the technical field of traffic flow prediction, in particular to a chaotic search optimization method for adaptive neural network traffic flow prediction. 【Background technique】 [0002] With the development of science and technology in our country to promote social progress, the scale of urbanization has expanded, and the number of private vehicles on the road has increased, resulting in frequent road traffic jams and traffic accidents, causing serious economic losses to the society, air pollution, excessive fuel consumption and other issues, which plague traffic travel. daily life of the patient. The intelligent transportation system is an effective means to alleviate road traffic congestion. In recent years, scholars at home and abroad have invested a lot of research on intelligent transportation, among which traffic flow prediction model is one of the most important research directions. Real-time and accurate traffic fl...

AI Technical Summary

Problems solved by technology

Sparrow Search Algorithm (SSA) is a swarm intelligence optimization algorithm newly proposed in 2020. The main idea of ​​SSA is to select a series of behaviors with optimal fitness according to a series of behaviors such as searching for food, competing for food, and avoiding predators. The research work shows that the algorithm is superior to particle swarm and gravity search algorithms in terms of convergence speed and accuracy, but the disadvantage is that the global optimization ability is poor

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