基于LightGBM和LSTM指纹定位算法的研究

Research on Fingerprint Localization Algorithm Based on LightGBM and LSTM

为进一步提高用户室外指纹定位的预测精度,提出了一种基于LightGBM(轻度提升机)算法和LSTM(长短期记忆神经网络)相结合的模型,通过分析比较基站收集到的用户信息,分别利用LightGBM与LSTM算法进行用户位置的预测,对两种方法预测的结果用误差倒数法减小误差同时对权重进行更新,得到结果作为新的预测值,在用户真实数据集上进行测试。测试结果表明,在对基站收集的用户信息进行指纹定位时,与其它模型相比,文章提出的模型在运行时间和预测精度方面均表现出良好的性能。

To further improve the prediction accuracy of user fingerprint location,the paper proposes a model based on Light Gradient Boosting Machine(LightGBM)algorithm and Long Short-Term Memory(LSTM)algorithm.By analyzing and comparing the user information collected by the base station,we used LightGBM algorithm and LSTM algorithm to predict user location respectively,and used the error reciprocal method to reduce the error of the predic-tion results of the two methods,updated the weight,and obtained the results as new prediction values,which were tested on the user's real data set.The test results show that,compared with other models,the model proposed in this paper shows good performance in running time and prediction accuracy.