本文關(guān)鍵詞：遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的短時交通流混沌預(yù)測，由筆耕文化傳播整理發(fā)布。

遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的短時交通流混沌預(yù)測 Chaotic prediction for short-term traffic ？ow of optimized BP neural network based on genetic algorithm

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摘要：

為了提高BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型對混沌時間序列的預(yù)測準確性,提出了一種基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的改進混沌時間序列預(yù)測方法.利用遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值,然后訓(xùn)練BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型以求得最優(yōu)解,并將該預(yù)測方法應(yīng)用到幾個典型混沌時間序列和實測短時交通流時間序列進行有效性驗證.仿真結(jié)果表明,該方法對典型混沌時間序列和短時交通流具有較好的非線性擬合能力和更高的預(yù)測準確性.

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.

  本文關(guān)鍵詞：遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的短時交通流混沌預(yù)測，由筆耕文化傳播整理發(fā)布。