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Reduced Type-2 Neural Fuzzy Classifers With Support Vector Learning And Human Body Posture Recongnition Applications
Neural Fuzzy Classifers
Support Vector Machine Learning
Body Posture Recongnition
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This thesis proposes a reduced interval type-2 neural fuzzy classifier with margin-selective gradient descent (MSGD) and support vector learning for antecedent and consequent part learning, respectively, and the classifier is called RIT2BFC-MS. The antecedent part in the RIT2BFC-MS uses interval type-2 fuzzy sets (IT2FSs) and the consequent part uses zero-order Takagi-Sugeno-Kang (TSK)-type rules with crisp or interval values. The RIT2BFC-MS uses weighted bound-set boundaries to simplify type-reduction operation and reduce training time. The RIT2BFC-MS is built through online structure learning. The antecedent parameters are tuned through MSGD learning to reduce training samples and avoid overtraining. The consequent parameters are learned through support vector machine to endow the classifier high generalization ability. The RIT2BFC-MS is applied to a vision-based human body posture classification problem. The classification system uses two cameras and novel features extracted from segmented human silhouettes to classify the four postures of standing, bending, sitting, and lying. The novel features include discrete Fourier transform (DFT) coefficients calculated from horizontal and vertical projections of the body silhouette and length-width ratio of human body. In addition, the minimum enclosing square of the body silhouette is uniformly partitioned and the area ratio of the silhouette in each partitioned grid is calculated used as another set of features. Classification performance of the RIT2BFC-MS is verified through the posture classification problem and several benchmark classification problems and comparisons with various type-1 and type-1 fuzzy classifiers.
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