Fault Detection and Isolation in Industrial Processes Based On RNN - Siva Kumar, A. P.;K, Chidananda;Pavan Kumar, N
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Deep Learning is a subset collection of Machine Learning concerned where neural network algorithms inspired by human brain learn from large amount of data through several layers for nonlinear transformation, Deep learning domain techniques allows to extract features automatically from the Industrial fault detection dataset. Since it has the strong learning ability that can improve the utilization of the dataset for the feature extraction. It determines Industrial Processes fault in the input data by simulating the way the human brain detects the fault in the industrial processes.

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Produktbeschreibung
Deep Learning is a subset collection of Machine Learning concerned where neural network algorithms inspired by human brain learn from large amount of data through several layers for nonlinear transformation, Deep learning domain techniques allows to extract features automatically from the Industrial fault detection dataset. Since it has the strong learning ability that can improve the utilization of the dataset for the feature extraction. It determines Industrial Processes fault in the input data by simulating the way the human brain detects the fault in the industrial processes.
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Autorenporträt
Dr. A.P.Siva Kumar, hizo su B.Tech de JNTUH, M.Tech de JNTUA, Ph.D de JNTUA en el área de "Information Retrieval and Cross Lingual Intelligent Systems" en el año 2011. Recibió el Premio Carrer para Jóvenes Profesores (CAYT) para el año fiscal 2013-14.