Abstract:

At present, in network optimization, network quality evaluation and prediction are generally based on communities, and a flexible evaluation system for POI network quality was proposed following the research idea of “research on dimensionality increase and implementation of dimensionality reduction”.However, it involves many network KPI, resulting in a relatively complex evaluation system for POI network comprehensive quality and low prediction accuracy.In order to improve the prediction accuracy of POI network quality, KPCA was used to compress the correlation of input variables of BP neural network, the structure of BP neural network was simplified, and then the connection weights and threshold parameters of BP neural network were optimized through GA.Compared with the prediction results of traditional BP neural network, the prediction accuracy is improved by 10.9%, and the mean square error performance is significantly reduced, it can play a better supporting role in the prediction of POI network quality.

Key words: POI flexibility evaluation system, kernel principal component analysis, genetic algorithm, BP neural network, POI quality prediction