Necessary and sufficient conditions are formulated for checking robust stability of an uncertain polynomial matrix. Various stability regions and uncertainty models are handled in a unified way. The conditions, stemmi...
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Necessary and sufficient conditions are formulated for checking robust stability of an uncertain polynomial matrix. Various stability regions and uncertainty models are handled in a unified way. The conditions, stemming from a general optimization methodology similar to the one used in μ-analysis, are expressed as a rank-one LMI, a non-convex problem frequently arising in robust control. Convex relaxations of the problem yield tractable sufficient LMI conditions for robust stability of uncertain polynomial matrices.
This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of E...
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This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of EEG signals were used: normal, schizophrenia (SCH), and obsessive compulsive disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66% of the normal class and 71% of the schizophrenia class of EEGs. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification.
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