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舰载通信系统本来就工作在弱信号区域,其通信效能的评估指标较多,各个指标在工作过程中并不完全独立,评估条件又受到工作区域恶劣环境的干扰,具有不确定性。传统的评估方法对舰载通信效能评估的过程中,只以各典型的属性作为评估指标,忽略某些模糊性指标间的相互作用,导致获取的评估结果不真实。提出一种基于RBF模糊神经网络的恶劣环境下的舰载通信效能评估优化模型,依据舰载通信系统的结构组成和影响系统的各种因素,建立舰载通信系统性能的指标体系,采用RBF模糊神经网络的方法建立舰载通信效能评估优化模型,在进行计算的过程中对权值以及隶属度进行自适应调整,对网络进行学习和训练,实现舰载通信效能评估。实验结果表明,采用所提算法对舰载通信系统进行效能评估,不仅评估结果准确有效,而且效率高,验证了该优化模型在恶劣环境下的舰载通信效能评估的有效性和可行性。
The carrier-based communication system has always been working in the weak signal area. There are many indicators of communication effectiveness evaluation. Each indicator is not completely independent in the working process, and the evaluation conditions are disturbed by the harsh environment in the working area, with uncertainties. In the process of traditional evaluation methods for the evaluation of shipborne communication effectiveness, only the typical attributes are used as evaluation indexes, and the interaction between some fuzzy indicators is ignored, resulting in the unrealistic assessment results obtained. This paper presents a model based on RBF fuzzy neural network to evaluate the carrier-based communication efficiency under harsh environment. Based on the structure of the carrier-borne communication system and various factors affecting the system, an index system of the performance of the carrier- Neural network to establish the optimization model of shipborne communication effectiveness evaluation. During the process of calculation, the weights and membership degrees are adaptively adjusted, and the network is learned and trained to evaluate the efficiency of carrier-based communication. The experimental results show that using the proposed algorithm to evaluate the effectiveness of the ship-borne communication system, not only the evaluation results are accurate and effective, but also the efficiency is high. The effectiveness and feasibility of the optimization model in the harsh environment of carrier-based communication performance evaluation are verified.