Author/Authors :
S.J. Lou، نويسنده , , H. Budman and
T.A. Duever، نويسنده ,
Latin Abstract :
Haar Wave-Net (HWN) and Projection Pursuit Regression (PPR) are two useful modeling tools for pattern classification. In this
study, the two methodologies are compared with respect to the problem of misclassification close to class boundaries with sparse
training data. A variety of examples were specifically tailored to elucidate their respective properties. It is observed that PPR locates
the class boundaries at the midline of two classes of training data, which is a logical choice for the class boundary location, in the
absence of sufficient information. For HWN, both the initial positioning of receptive fields and the density of training data near the
class boundary may have great impact on the definition of the class boundary. Additionally, PPR and HWN are also compared to
the Backpropagation Network (BPN), a standard technique for fault detection, with respect to their sensitivity to noise. The
orthonormal and localized properties of the Haar basis functions enable a HWN to limit the noise effect within its local receptive
fields. BPN propagates the noise effect throughout the input space. PPR provides a good tradeoff between reasonable generalization
and noise localization. The fault diagnosis problem is investigated in a CSTR process, at both steady state and dynamic conditions.
It is found that, for the dynamic case, the misclassification close to the class boundary is often due to lack of system observability.
NaturalLanguageKeyword :
Haar Wave-Net , pattern classification , Backpropagation network , noise , Projection pursuit regression
