基于遗传神经网络的货运量预测的研究与实现

针对BP网络和遗传算法的缺陷，提出了一种新的遗传神经网络优化模型，可以用来同时优化BP神经网络的网络结构和权值阈值，叙述了该算法的设计过程。利用该算法对货运量进行预测，并与标准的BP算法预测结果进行比较，计算结果表明，该算法的预测精度大大高于标准的BP算法的预测精度。

Freight Prediction Based on Genetic Neural Network

To deal with the defects of BP neural network and genetic algorithm, this paper presents a new optical model of BP neural Network, which can optimize the structure, weights and biases of neural network simultaneously. The paper describes the design process of the algorithm and uses the algorithm to predict the freight. Compared with the results predicted according to standard BP algorithm, the prediction results is much more precise.