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Neural Network Learning Enhancement using Island Model based Differential Evolution Algorithm
https://meral.edu.mm/records/4741
https://meral.edu.mm/records/474138dee133-8054-427b-927f-4f76604d26c5
d56a950c-c1bc-45e3-89c5-6294bd241039
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Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Neural Network Learning Enhancement using Island Model based Differential Evolution Algorithm | |||||
Language | en | |||||
Publication date | 2014-02-17 | |||||
Authors | ||||||
Tun, Khin Mo Mo | ||||||
Thein, Htet Thazin Tike | ||||||
Description | ||||||
Classification is a machine learning techniqueused to predict group membership for data instances.To simplify the problem of classification neuralnetworks are being introduced. In this paper, theadaptation of network weights using Island Modelbased Differential Evolution (IMDE) was proposedas a mechanism to improve the performance ofArtificial Neural Network (ANN). DifferentialEvolution (DE) has been used to determine optimalvalue for ANN parameters such as learning rate andmomentum rate and also for weight optimization.Island model used multiple subpopulations andexchanges the individual to boost the overallperformance of the algorithm. In this paper, fullyconnected topology is being used. This systemproposes an island model based differential evolutionalgorithm to enhance the learning speed of neuralnetwork training. The results have revealed thatIMDENN has given quite promising results in termsof convergence rate smaller errors compared to otheralgorithms. | ||||||
Keywords | ||||||
Artificial neural network, Differential Evolution, Particle Swarm Optimization, Genetic Algorithm, Island Model | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/47 | |||||
Journal articles | ||||||
Twelfth International Conference On Computer Applications (ICCA 2014) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |