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Independent neural network modeling of class analogy for classification pattern recognition and optimization
An independent neural network modeling of class analogy (INMCA) has been proposed as a classification pattern recognition method, which combines the idea of the classical soft independent modeling of class analogy (SIMCA) with the back-propagation neural network (BPN). The INMCA can not only exclude...
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Published in: | Analytica chimica acta 1997-04, Vol.342 (2), p.223-228 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | An independent neural network modeling of class analogy (INMCA) has been proposed as a classification pattern recognition method, which combines the idea of the classical soft independent modeling of class analogy (SIMCA) with the back-propagation neural network (BPN). The INMCA can not only exclude noise samples and select useful features in the multivariate calibration of complicated chemical processes, but also provide the class centers in the non-linear space for optimization of a chemical process. The data processing of a silicon steel process, as an application example, shows this INMCA to be useful. |
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ISSN: | 0003-2670 1873-4324 |
DOI: | 10.1016/S0003-2670(96)00531-4 |