Research on fusion algorithm with BP neural network and rough set

Considering that the neural network increases rapidly in complexity and is greatly extended in training time due to the gradually increasing input vectors, a fusion algorithm modified by rough set is proposed to preprocess the inputs before neural network. Firstly, the input sample space is reduced...

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Bibliographic Details
Published in2011 6th IEEE Conference on Industrial Electronics and Applications pp. 118 - 123
Main Authors Li Wei, Chen Ming, He Peng-ju, Jiang Li-jun, Zhang Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Artificial neural networks
attribute significance
Biological neural networks
BP neural network
Electronic mail
fusion algorithm
Monitoring
rough set
Sensitivity
Training
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Summary:Considering that the neural network increases rapidly in complexity and is greatly extended in training time due to the gradually increasing input vectors, a fusion algorithm modified by rough set is proposed to preprocess the inputs before neural network. Firstly, the input sample space is reduced to obtain the new decision table according to attribute significance in rough set. Then the reduced decision table is applied to train the BP neural network until it converges. Finally, the algorithm is used for classification in car mileage level. The classification accuracy of the testing sample is increased by 10% than the traditional BP neural network fusion algorithm. The result shows the modified fusion algorithm is feasible.
ISBN:9781424487547
1424487544
ISSN:2156-2318
2158-2297
DOI:10.1109/ICIEA.2011.5975561