@article{(Open Science Index):https://publications.waset.org/pdf/13943, title = {Evolving Neural Networks using Moment Method for Handwritten Digit Recognition}, author = {H. El Fadili and K. Zenkouar and H. Qjidaa}, country = {}, institution = {}, abstract = {This paper proposes a neural network weights and topology optimization using genetic evolution and the backpropagation training algorithm. The proposed crossover and mutation operators aims to adapt the networks architectures and weights during the evolution process. Through a specific inheritance procedure, the weights are transmitted from the parents to their offsprings, which allows re-exploitation of the already trained networks and hence the acceleration of the global convergence of the algorithm. In the preprocessing phase, a new feature extraction method is proposed based on Legendre moments with the Maximum entropy principle MEP as a selection criterion. This allows a global search space reduction in the design of the networks. The proposed method has been applied and tested on the well known MNIST database of handwritten digits.}, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {11}, year = {2007}, pages = {3508 - 3511}, ee = {https://publications.waset.org/pdf/13943}, url = {https://publications.waset.org/vol/11}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 11, 2007}, }