防空导弹无线电引信目标识别优化分析

Analysis on Radil Fuse Target Identification Optimization of Ground-to-air Missile

提出了用人工免疫算法优化RBF网络隐含层的性能参数,以及用最小二乘法确定RBF网络的线性输出层的权值,建立了基于人工免疫算法的两级RBF网络混合训练学习的算法机制;采用所建立的两级RBF网络混合训练学习机制对引信的定向探测进行了优化研究,并通过计算机仿真给出了该方法在目标方位识别方面的优越性。

The parameter optimization of the RBF nerve network through artificial immune algorism is proposed, and the weight of RBF network linear output layer through least aquaria method is confirmed. The hybrid train and learn algorism of two-layer RBF network based on artificial immune algorism is built; the proposed method is used to optimize the oriented detection of radio fuze; and the superiority of the optimization method is given through the computer simulation.