2011Kongzhi yu jueceRequires access

Chaotic prediction for short-term traffic ?ow of optimized BP neural network based on genetic algorithm

Yongle Xie

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Abstract

In order to improve the prediction accuracy of BP neural network model for chaotic time series,a prediction method for chaotic time series of optimized BP neural network based on genetic algorithm(GA) is presented.The GA is used to optimize the weights and thresholds of BP neural network,and the BP neural network is trained to search for the optimal solution.The efficiency of the proposed prediction method is tested by the simulation of several typical nonlinear systems and time series of real traffic ?ow.The simulation results show that the proposed method has better fitting ability and higher accuracy.

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What this paper is about

In order to improve the prediction accuracy of BP neural network model for chaotic time series,a prediction method for chaotic time series of optimized BP neural network based on genetic algorithm(GA) is presented.The GA is used to optimize the weights and thresholds of BP neural network,and the BP neural network is trained to search for the optimal solution.The efficiency of the proposed prediction method is tested by the simulation of several typical nonlinear systems and time series of real traffic ?ow.The simulation results show that the proposed method has better fitting ability and higher accuracy.

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Available abstract

In order to improve the prediction accuracy of BP neural network model for chaotic time series,a prediction method for chaotic time series of optimized BP neural network based on genetic algorithm(GA) is presented.The GA is used to optimize the weights and thresholds of BP neural network,and the BP neural network is trained to search for the optimal solution.The efficiency of the proposed prediction method is tested by the simulation of several typical nonlinear systems and time series of real traffic ?ow.The simulation results show that the proposed method has better fitting ability and higher accuracy.

Key concepts: Artificial neural network, Chaotic, Genetic algorithm, Series (stratigraphy), Algorithm, Computer science, Nonlinear system, Term (time)

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