改进神经网络的无线网络室内定位

受到多种因素的干扰，室内定位一直是无线网络研究中的热点，为了提升无线网络室内定位的效果，针对当神经网络存在无线网络室内定位精度的难题，设计了一种基于改进神经网络的无线网络室内定位方法. 首先收集无线网络室内相关信息，提取室内定位的数据，然后采用神经网络对数据进行学习，建立无线网络定位模型，并对神经网络的缺陷进行改进，最后在Matlab平台上进行了仿真实验. 结果表明，改进神经网络克服传统室内定位方法存在的局限性，获得了更高的无线网络室内定位精度，而且室内定位效率也得到了明显的改善.

Indoor Positioning of Wireless Network Based on Improved Neural Network

Interfered by a variety of factors, indoor positioning has been a research hotspot in wireless network. To improve the indoor positioning effect, aiming at the problem that the neural network has in indoor positioning accuracy of the wireless network, this paper designs a wireless network based on artificial neural networks. The first indoor wireless network collects relevant information, extracts indoor positioning data, and then uses neural network for data learning. It sets up a wireless network positioning model to improve the defects of the neural network. Finally, the simulation is carried out on the Matlab platform. The results show that the improved neural network overcomes the limitations of the traditional indoor localization methods, and achieves higher indoor localization accuracy of wireless networks. Moreover, the indoor localization efficiency has also been improved significantly.