April 2000

Computer Modeling/Control

Improve nonlinear soft sensing modeling by combining multiple models

FMM-based methods are shown to give better results

Yu, J., Zhong, W., Research Institute of Automation, East China University of Science and Technology

Here's a proposal for a new method for nonlinear soft sensing modeling of chemical processes. This method is inspired by the concept of combining models to improve prediction accuracy and robustness. A fuzzy c-means clustering (FCM) algorithm is used for separating a whole training data set into several clusters with different centers. Each subset is trained by radial base function networks (RBFN) or a partial least squares (PLS) algorithm, and the degrees of membership are used for combining several models to achieve the final result. The PLS algorithm has been found preferable in handling limited data, and RBFN perform

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