A novel radar signal recognition method based on a deep restricted Boltzmann machine

Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on the deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. This model is composed of multiple restricted Boltzmann machines. A bottom-up hierarchical unsupervised learning is used to obtain the initial parameters, and then the traditional back propagation (BP) algorithm is conducted to fine-tune the network parameters. Softmax algorithm is used to classify the results at last. Simulation and comparison experiments show that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it is characterized with strong robustness as well as highly correct recognition rate

Energy-Based Models in Document Recognition and Computer Vision.

The Deep Learning Problem is related to the issue oftraining all the levels of a recognition system (e.g. segmentation, feature extraction, recognition, etc) in an inte-grated fashion. We first consider "traditional " methods for deep learning, such as convolutional networks and back-propagation, and show that, although they produce very low error rates for handwriting and object recognition, they re-quire many training samples. We show that using unsupervised learning to initialize the layers of a deep network dra-matically reduces the required number of training samples, particularly for such tasks as the recognition of everydayobjects at the category level. 1. Two Challenges in Machine Learning It may come to a surprise to many members of the IC-DAR community that some of the recent advances in Machine Learning have their root in the document recognitionliterature. What used to be called statistical pattern recognition, and has become the core of the expanding field of ma-chine learning (ML), has always been a key component of document recognition systems. But for many other areas ofcomputer and information science, such as natural language processing, computer vision, and robotics, the widespreadadoption of ML methods is relatively recent. Interestingly, there are at least two important classes ofML methods that were first developped in the context o