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.

    本发明提出了一种自适应神经网络交通流预测的混沌搜索优化方法,包括以下步骤:S1.构建BP神经网络模型,初始化网络参数;S2.初始化麻雀算法各项参数;S3.加入Tent混沌映射初始化种群;S4.计算种群中麻雀适应度值;S5.根据适应度值对种群进行排序;S6.生产者位置更新;S7.跟随者位置更新;S8.危险时,麻雀位置更新;S9.先进行个体最优适应度值更新,再进行群体最优适应度值更新,进入步骤S10;S10.判断是否达到迭代次数,若未达到则返回至步骤S5;否则,输出最优适应度值和全局最优位置,进入步骤S11;S11.将最优适应度值和全局最优位置赋予BP神经网络模型,优化其权值和阈值,进行预测,完成CSSA‑BP模型的搭建。该方法预测精确度更高,迭代速度更快。


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    Title :

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


    Additional title:

    一种自适应神经网络交通流预测的混沌搜索优化方法


    Contributors:
    LOU JUNGANG (author) / WANG JINGYUE (author) / SHEN QING (author) / MAO LI'AN (author)

    Publication date :

    2022-02-08


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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