With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission un-reliabili...
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With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission un-reliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deepcomputationmodel achieves superior performance for big data feature learning. However, it is difficult for the deepcomputationmodel to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deepcomputationmodel to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.
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