基于遗传ABC算法的改进RBF神经网络协同停机检测方法

Yuting Wang, Peng Long, Nan Liu, Zhiwen Pan, X. You
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引用次数: 5

摘要

无线网络的中断检测是无线网络自修复的一个重要问题。本文提出了一种基于遗传人工蜂群(IRBFG)算法改进的RBF神经网络的协同停机检测范式,以实现神经网络参数的全局优化和对非线性用户数据的更好分类。通过改进的决策树基础学习器选择空间和时间特征以获得更好的性能。仿真结果表明,该方案具有较高的检测精度和较低的数据传输速率,特别是在密集的小蜂窝网络环境下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Cooperative Outage Detection Approach Using an Improved RBF Neural Network with Genetic ABC Algorithm
Outage detection in wireless networks is a significant problem of self-healing in SON. In this paper, we propose a cooperative outage detection paradigm using the RBF neural network improved by a genetic artificial bee colony(IRBFG) algorithm for global optimum of neural network parameters and better classification of nonlinear user data. Spatial and temporal features are selected through an improved decision tree base learner for better performance. The simulation results demonstrate that the proposed scheme receives higher detection accuracy and reduces data transmission, especially in the dense small cell network environment.
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