An incremental neural learning framework and its application to vehicle diagnostics |
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Authors: | Yi L Murphey Zhi Hang Chen Lee A Feldkamp |
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Affiliation: | (1) Department of Electrical and Computer Engineering, The University of Michigan-Dearborn, Dearborn, MI 48128-1491, USA;(2) Research and Advanced Engineering, Ford Motor Company, Dearborn, MI 48121, USA |
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Abstract: | This paper presents a framework for incremental neural learning (INL) that allows a base neural learning system to incrementally
learn new knowledge from only new data without forgetting the existing knowledge. Upon subsequent encounters of new data examples,
INL utilizes prior knowledge to direct its incremental learning. A number of critical issues are addressed including when
to make the system learn new knowledge, how to learn new knowledge without forgetting existing knowledge, how to perform inference
using both the existing and the newly learnt knowledge, and how to detect and deal with aged learnt systems. To validate the
proposed INL framework, we use backpropagation (BP) as a base learner and a multi-layer neural network as a base intelligent
system. INL has several advantages over existing incremental algorithms: it can be applied to a broad range of neural network
systems beyond the BP trained neural networks; it retains the existing neural network structures and weights even during incremental
learning; the neural network committees generated by INL do not interact with one another and each sees the same inputs and
error signals at the same time; this limited communication makes the INL architecture attractive for parallel implementation.
We have applied INL to two vehicle fault diagnostics problems: end-of-line test in auto assembly plants and onboard vehicle
misfire detection. These experimental results demonstrate that the INL framework has the capability to successfully perform
incremental learning from unbalanced and noisy data. In order to show the general capabilities of INL, we also applied INL
to three general machine learning benchmark data sets. The INL systems showed good generalization capabilities in comparison
with other well known machine learning algorithms. |
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Keywords: | Incremental learning Neural networks Vehicle diagnostics |
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