Comparison between MSE and MEE Based Component Extraction Approaches to Process Monitoring and Fault Diagnosis
نویسندگان
چکیده
Component extraction is a technique for extracting the latent components that underlie the observation of a set of variables. In the paper both classical Principal component analysis (PCA) and autoassociative principal component neural network (PCNN) methods with minimum mean square error (MSE) criterion are compared with the corresponding extended component extraction methods with Minimum error entropy (MEE) criterion in theory. A Parzen window estimator based approximative computation method for entropy is provided, and the equivalence between MSE and MEE criteria is also analyzed.
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