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利用反辐射导弹来摧毁敌方雷达系统和有关设施是现代战争中一种有效的对抗手段。但是,在目前辐射源增多、信号密集的情况下,传统的信号分选和识别方式都遇到了困难,促使人们去研究以并行分布处理为特征的神经网络理论,探求一种(?)的智能信息处理方法和识别途径来解决这一难题。本文正是从这一点出发,利用BSB(脑中盒状态)模型来完成反辐射导弹的辐射源识别。计算机模拟结果证明这种方法是行之有效的。
The use of anti-radiation missiles to destroy enemy radar systems and related facilities is an effective means of confrontation in modern warfare. However, with the current increase of radiation sources and signal intensiveness, the traditional methods of signal sorting and identification have encountered difficulties and prompted people to study the theory of neural networks characterized by parallel distributed processing to explore a (?) Intelligence Information processing methods and identification of ways to solve this problem. Starting from this point, this paper uses the BSB model to complete the radiation source identification of anti-radiation missiles. Computer simulation results prove that this method is effective.