Abstract
This paper presents a new wavelet-network-based technique for 1D and 2D functions' approximation. Classical training algorithms start with a predetermined network structure which can be either insufficient or overcomplicated. Furthermore, the resolutions of wavelet networks training problems by gradient are characterized by their noticed inability to escape of local optima.
The main feature of this technique is that it avoids both insufficiency and local minima by including genetic algorithms. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Multi Mother Wavelet Neural Network based on genetic algorithms.