Modeling switched circuits based on wavelet decomposition and neural networks

In this paper, switched circuits are modeled based on wavelet decomposition and neural network. Also describes the usage of wavelet decomposition and neural network for modeling and simulation of nonlinear systems. The switched circuits are piecewise-linear circuits. At each position of switch the circuit is linear but when considered all switching positions of the circuit it becomes nonlinear. An important problem which arises in modeling switched circuit is high structural complexity. In this study, wavelet decomposition is used for feature extracting from input signals and neural network is used as an intelligent modeling tool. Two performance measures root-mean-square (RMS) and the coefficient of multiple determinations (R2) are given to compare the predicted and computed values for model validation. The evaluated R2 value is 0.9985 and RMS value is 0.0099. All simulations showed that the proposed method is more effective and can be used for analyzing and modeling switched circuits. When we consider obtained performance, we can easily say that the proposed method can be used efficiently for modeling any other nonlinear dynamical systems.