Abstract
This paper proposes dynamic kernel principal components analysis (DKPCA) approach to bioprocesses monitoring. The basic idea of KPCA is to map the input data into a feature space first via a nonlinear mapping, and then perform a linear PCA in feature space F . The dynamic kernel matrix of DKPCA can capture the nonlinearity and the dynamics of bioprocesses. The proposed method was applied to the fault detection and diagnosis of a simulation benchmark of a biological treatment process. The simulation results clearly show the effectiveness of the proposed approach.
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