Aggregate volumetric estimation based on PCA and momentum-enhanced BP neural network |
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Authors: | Ken Chen Pan Zhao Celal Batur Yun Zhang |
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Affiliation: | 1. College of Information Science and Engineering,Ningbo University,Ningbo 315211,China 2. College of Engineering,The University of Akron,Ohio 44325,USA |
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Abstract: | This paper proposes a Back Propagation (BP) neural network with momentum enhance-ment aiming to achieving the smooth convergence for aggregate volumetric estimation purpose. Net-work inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image. To simplify the network structure, principal component analysis technique is applied to reduce the input dimension. The specific network structure is finalized based on both em-pirical expertise and analysis on selecting the appropriate number of neurons in hidden layer. The network is trained using the finite number of randomly-picked particles. The training and test results suggest that, compared to the generic BP network, the training duration of the proposed neural network is greatly attenuated, the complexity of the network structure is largely reduced, and the es-timation precision is within 2%, being sufficiently up to technical satisfaction. |
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Keywords: | Aggregate volume Back Propagation (BP) neural network Momentum Volume estimate Principal Component Analysis (PCA) |
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