Wavenet Algorithms with Applications in Approximation Signals: A Comparative Study

In this paper adaptable methods for computational algorithms are presented. These algorithms use neural networks and wavelet series to build neuro wavenets approximators. The algorithms obtained are applied to the approximation of signals that represent algebraic functions and random functions, as well as a medical EKG signal. It shows how wavenets can be combined with auto-tuning methods for tracking complex signals that are a function of time. Results are shown in numerical simulation of two architectures of neural approximators wavenets: the first is based on a wavenet with which they approach the signals under study where the parameters of the neural network are adjusted online, the other neuro approximator scheme uses an IIR filter to the output of wavenet, which serves to filter the out- put, in this way discriminate contributions of neurons that are less important in the approximation of the signal, which helps reduce the convergence time to a desired minimum error.