Differential evolution for neural networks learning enhancement.

Sttar Ismail Wdaa, Abdul

doi:10.37652/juaps.2011.44119

	Differential evolution for neural networks learning enhancement.
Journal of University of Anbar for Pure Science
Article 20, Volume 5, Issue 2, August 2011, Pages 79-84 PDF (488.92 K)
Document Type: Research Paper
DOI: 10.37652/juaps.2011.44119
Author
Abdul Sttar Ismail wdaa^*
College of Education for pure sciences- University of Anbar
Abstract
In this paper ,we use new treatment ,Differential Evolution,, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. The results have revealed that DENN has given quite promising results in terms of convergence rate and smaller errors compared to PSONN and GANN.
Keywords
Differential evolution; neural networks; learning enhancement

References
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