基于RBM-GASA-BPNN的潜在 高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足，提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别，设置类别标签；然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围，又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调，确定了最优的BPNN初始权值和偏置；最后，利用优化后的BPNN对旅客进行分类预测.实验结果表明，本文提出的方法克服了基于BPNN的分类预测方法的缺陷，具有更高的分类预测准确率和潜在高价值旅客预测能力.

Potential High Value Passenger Forecast Based on RBM-GASA-BPNN

Aiming at the problems of weak feature expression ability, poor stability and higher local optimal probability of the potential high-value passengers discovery method based on BP neural network (BPNN), a novel potential high value passengers discovery method based on RBM-GASA-BPNN is proposed in this paper. Firstly, clustering algorithm is used to classify passengers and set category labels. Then the restricted Boltzmann machine (RBM) is used to automatically extract the passenger's behavior features and provide the optimal range of initial weight and bias for BPNN. And the genetic algorithm-simulated annealing (GASA) algorithm is used to adjust the parameters precisely to find the optimal initial weight and bias of BPNN. Finally, the optimized BPNN is used to classify passengers. The experimental results show that the proposed method overcomes the shortcomings of existing method based on BPNN and has a better classification prediction accuracy and potential high value passenger forecast ability.