Publication Type : Journal Article
Publisher : Academy Publishers, Finland
Source : International Journal of Recent Trends in Engineering, , Academy Publishers, Finland, Volume 1, Issue 2, p.84-89 (2009)
Url : https://pdfs.semanticscholar.org/f484/1d3e6130f65ee9db4779e24513d81b5d6527.pdf
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Verified : Yes
Year : 2009
Abstract : Neural Network (ANN) whose weights are genetically evolved using the proposed Enhanced Genetic Algorithm (EGA), thereby obtaining optimal weight set. The performance is analysed by fitness function based ranking. The ability of learning may depend on many factors like the number of neurons in the hidden layer, number of training input patterns and the type of activation function used. By varying each parameter, the performance of the proposed EGA algorithm is compared with normal NN training.
Cite this Research Publication : N Mohankumar, NirmalaDevi, M., Karthick, M., Jayan, N., Nithya, R., Shobana, S., M Sundar, S., and Arumugam, S., “Design of Genetically Evolved Artificial Neural Network Using Enhanced Genetic Algorithm”, International Journal of Recent Trends in Engineering, vol. 1, no. 2, pp. 84-89, 2009.