Intelligent Signal Processing Using Wavelet And Neural Network - Lee, Ian Wen Chun (2003)
This thesis presents a novel approach for the detection, classification and data mining of non-stationary signals in power networks by combining the S-Transform and neural networks. The S-Transform provides frequency dependent resolution that simultaneuously localizes the real and imaginary spectra. The S-Transform is similar to the Wavelet Transform but with a phase correction. This property is used to obtain useful features of the non-stationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-Transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks.