和声搜索算法优化神经网络的无线网络室内定位

摘要

Indoor environment is complex and changeable,and wireless signal has strong time-varying.Support vector machine has low positioning efficiency while neural network is difficult to determine the parameter.In order to improve the positioning performance in wireless network,a novel wireless positioning algorithm based on harmony search algorithm optimizing neural network is proposed.Firstly,training samples of wireless network are collected and the size of training samples is reduced by a compressed sensing algorithm;secondly,clustering algorithm is used to cluster the samples;finally,harmony search algorithm is used to optimize neural network and feasibility is tested by simulation experiments.Test results show that the positioning results of the proposed algorithm can meet the actual requirements of wireless network positioning.%室内环境复杂多变,无线信号具有强烈的时变性,支持向量机存在定位效率低,神经网络参数难以确定等难题.为了改善无线网络室内的定位效果,提出了和声搜索算法优化神经网络的无线网络室内定位模型.首先收集无线网络定位的训练样本,采用压缩感知算法减少训练样本的规模,然后采用聚类算法对样本进行聚类分析,选择最有效的训练样本,最后采用和声搜索算法优化神经网络实现无线网络定位,并通过具体仿真对比实验测试了该算法的可行性.测试结果表明,该算法的定位效果可以满足无线网络的定位实际要求.