**2. Wavelet networks**

The combination of wavelet principle and neural networks has led to producing new representing network of wavelet neural network (WNN). Wavelet networks are feedforward networks utilizing wavelets as activation functions. Wavelet networks substitute the sigmoid activation components of the classical feedforward artificial neural networks (ANNs) with wavelets transform function. In wavelet neural networks, both the translation (position) and the dilation are tuning besides weights. The utilization of wavelet node outcomes in efficient networks are optimally approximated and estimated for nonlinear and nonstationary functions [11, 12]. There are two main types to construct the wavelet neural network:

