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Cascade-Correlation Neural Network for Sensor Fault Detection and Data Recovery with On-line Learning

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Cascade-Correlation (CC) is a new architecture and supervised learning algorithm for artificial neural networks. The fundamental theory of the Cascade-Correlation neural network is firstly introduced, then a novel method based on the Cascade-Correlation neural network with on-line learning is proposed, which is used in sensor fault detection and data recovery, and the specific procedure of the method is described in detail. Finally, this method is applied to a six-component force/torque sensor, and compared with Back Propagation (BP) neural network predictor, the experimental results show that the proposed method has higher prediction and recovery accuracy and consumes less time than a BP neural network. Therefore, the proposed method is suitable and very effective for sensor fault detection and short-term data recovery.


Document Type: Research Article


Publication date: 2011-10-01

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  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
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