Nichols Res. Corp., Lexington, MA.
IEEE Trans Image Process. 1997;6(1):203-16. doi: 10.1109/83.552107.
A controversial issue in the research of mathematics of intelligence has been that of the roles of a priori knowledge versus adaptive learning. After discussing mathematical difficulties of combining a priority with adaptivity encountered in the past, we introduce a concept of a model-based neural network, whose adaptive learning is based on a priori models. Applications to target detection in SAR images are discussed. We briefly overview the SAR principles, derive relatively simple physics-based models of SAR signals, and describe model-based neural networks that utilize these models. A number of real-world application examples are presented.
智能数学研究中的一个有争议的问题是先验知识与自适应学习的作用。在讨论了过去在将先验知识与适应性相结合时遇到的数学困难之后,我们引入了基于模型的神经网络的概念,其自适应学习基于先验模型。讨论了在 SAR 图像目标检测中的应用。我们简要概述了 SAR 原理,推导了 SAR 信号的相对简单的物理模型,并描述了利用这些模型的基于模型的神经网络。给出了一些实际应用的例子。