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针对传统RBF神经网络存在的高维数据学习训练问题,采用K-means聚类算法设计RBF神经网络数据中心,建立基于聚类RBF神经网络的机载传感器精度评估模型,运用改进的RBF神经网络对机载传感器系统进行精度评估研究。仿真研究结果表明,与传统RBF神经网络评估算法相比,该算法有效减少评估时间,提高预测精度,表明算法是合理和有效的。
In order to solve the problem of traditional RBF neural network training problem, RBF neural network data center is designed by using K-means clustering algorithm, and airborne sensor accuracy assessment model based on RBF neural network is established. By using improved RBF neural network Airborne sensor system to evaluate the accuracy. Simulation results show that compared with the traditional RBF neural network evaluation algorithm, the proposed algorithm can effectively reduce the evaluation time and improve the prediction accuracy. It shows that the algorithm is reasonable and effective.