Research on target location prediction based on improved PSO-RBF Neural Network

Zhan Wang, Shuang Xia, Hua Yu, Yangchun Wang
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Abstract

In order to make the antenna point to the target position in real time and obtain the current target parameters, an improved PSO-RBF neural network for antenna target position prediction was proposed. Based on the RBF neural network model, the improved PSO-RBF algorithm was used to optimize the network parameters, and the prediction model was established on the basis of the measured data. Simulation results show that the prediction effect of this model is better than traditional RBF neural network and conventional PSO-RBF neural network, and it has better practical value.
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基于改进PSO-RBF神经网络的目标位置预测研究
为了使天线实时指向目标位置并获取当前目标参数,提出了一种改进的PSO-RBF神经网络用于天线目标位置预测。在RBF神经网络模型的基础上,采用改进的PSO-RBF算法对网络参数进行优化,并基于实测数据建立预测模型。仿真结果表明,该模型的预测效果优于传统的RBF神经网络和传统的PSO-RBF神经网络,具有较好的实用价值。
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