LEARNING NETWORKS FOR EXTRAPOLATION AND RADAR TARGET IDENTIFICATION
LEARNING NETWORKS FOR EXTRAPOLATION AND RADAR TARGET IDENTIFICATION
复制标题
DOI：
10.1016/0893-6080(92)90013-9
复制
发表时间：
1992-01-01
期刊：
影响因子：
7.8
通讯作者：
FARHAT, NH
中科院分区：
文献类型：
--
作者：
BAI, BC;FARHAT, NH
本文讨论了用神经网络从部分频率响应数据中进行近似重建和目标识别的外推问题。由于不适定性，传统上用正则化方法处理该问题。研究了正则化和隐层神经元在分层神经网络中的作用之间的关系，建立了一个具有良好鲁棒性的分层非线性自适应神经网络进行外推和重构。结果，然后扩展到neuromorphic目标识别从一个单一的“看”（单宽带雷达回波）。文中还提出了一种新的方法，利用真实的实验数据，在具有良好鲁棒性的学习网络中实现100%的正确识别。所报告的调查结果可能会证明需要形成雷达图像以识别目标，并可能为识别非合作目标提供一种可行和经济的手段。
The problem of extrapolation for near-perfect reconstruction and target identification from partial frequency response data by neural networks is discussed. Because of ill-posedness, the problem has traditionally been treated with regularization methods. The relationship between regularization and the role of hidden neurons in layered neural networks is examined, and a layered nonlinear adaptive neural network for performing extrapolations and reconstructions with excellent robustness is set up. The results are then extended to neuromorphic target identification from a single "look " (single broad-band radar echo). A novel approach for achieving 100% correct identification in a learning net with excellent robustness employing realistic experimental data is also given. The findings reported could potentially obviate the need to form radar images in order to identify targets and could furnish a viable and economical means for identifying noncooperative targets.