A New Approach to Time–Time Transform and Pattern Recognition of Non-stationary Signal Using Fuzzy Wavelet Neural Network

Abstract

This paper discusses new approaches in time- time transform and Nonstationary power signals classification using fuzzy wavelet neural networks. The time-time representation is derived from the S-transform, a method of representation of a real time series as a set of complex, time-localized spectra. When integrated over time, the S-transform becomes the Fourier transform of the primary time series. Similarly, when summed over the primary time variable, the TT-transform reverts to the primary time series. TT-transform points to the possibility of filtering and signal to noise improvements in the time domain. In our research work visual localization, detection and classification of Nonstationary power signals problem using TT-transform and automatic Nonstationary power signal classification using FWNN (Fuzzy wavelet Neural Network) have been considered. Time-time analysis and Feature extraction from the Nonstationary power signals is done by TT-transform. In the proposed work pattern recognition of various Nonstationary power signals have been considered using particle swarm optimization technique. This paper also emphasizes the robustness of TT-transform towards noise. The average classification accuracy of the noisy signals due to disturbances in the power network is of the order 92.1.