Artificial Neural Network Learning Using Improved Genetic AlgorithmsJournal of The Institution of Engineers (India), CP (2001)
AbstractThe feedforward back-propagation artificial neural networks (ANN) are widely used to control the various industrial process, for modelling, simulation of systems and forecasting. The backpropagation learning has various drawbacks such as slowness in learning, stuck in local minima, requies functional derivative of aggregation function and thresholding function to minimize error function. Various researchers have suggested a number of improvement in simple back-propagation learning algorithm developed by Widrow and Holf in 1956. In this paper, a program is developed for feedforward artificial neural network with genetic algorithm (GA) as the learning mechanism to overcome some of the disadvantages of back-propagation learning mechanicsm to minimize the error function of ANN. Genetic Algorithm (GA) simulates the strategy of evolution and survival of fittest. It is a powerful domain free approach that has been integrated with ANN as a learning tool. The ANN-GA integrated approach is applied to different problems to test this approach. GA optimization is slow and depends on the number of variables. To imporve the convergence of GA, a modified Ga is developed in which the GA parameters are modified using five fuzzy rules with concentration genes.
- Genetic Algorithms,
Publication DateNovember, 2001
Citation InformationD. K. Chaturvedi. "Artificial Neural Network Learning Using Improved Genetic Algorithms" Journal of The Institution of Engineers (India), CP Vol. 82 (2001)
Available at: http://works.bepress.com/dk_chaturvedi/25/