A Robust Signal Estimation Using Wavelet Neural Network

Signal Estimation has been a crucial issue in many engineering contexts including information physical and cyber security, image processing, robotics, automatic control, power system identification, and etc. This paper presents a signal prediction approach using wavelet network (WN). WN is defined as a class of neural network associated with wavelet functions that encompasses two steps of translation and dilation. To exhibit the strengths of the proposed WN, the simulation results are compared with three renowned predictors including autoregressive moving average (ARMA), recurrent neural network (RNN) and chaotic neural network (CNN). Based on the results found, the proposed WN predictor outperforms in terms of estimation signal precision and quickness of the execution time. Moreover, provided WN Network with simple and robust estimation mechanism allows one to efficiently solve other complex nonlinear signal processing problems such as system identification and adaptive equalization.