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为提高BP神经网络预测模型的预测准确性,提出了一种基于改进遗传算法优化BP神经网络的预测方法.通过设计多层阶梯结构染色体,改进编码方式、适应度函数和遗传算子,引入自适应交叉、变异概率,实现对BP神经网络网络结构和初始网络权重的同步全局优化,提高BP神经网络预测模型的非线性学习和泛化能力.将该预测方法应用到黄山风景区日客流量实际预测中进行有效性验证,结果表明该方法对旅游景区日客流量具有更好的非线性拟合能力和预测准确性.
In order to improve the prediction accuracy of the BP neural network prediction model, a prediction method based on the improved genetic algorithm is proposed to optimize the BP neural network. By designing the chromosomes of multilevel ladder structure, improving the encoding method, fitness function and genetic operator, Adapt to the crossover and mutation probability and realize the simultaneous global optimization of the BP neural network network structure and the initial network weight and improve the nonlinear learning and generalization ability of the BP neural network prediction model.Application of this prediction method to the actual daily traffic of Huangshan Scenic Area The results show that this method has better nonlinear fitting ability and forecasting accuracy for daily passenger flow of tourist attractions.