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Neural Network Learning Enhancement using Island Model based Differential Evolution Algorithm

https://meral.edu.mm/records/4741
38dee133-8054-427b-927f-4f76604d26c5
d56a950c-c1bc-45e3-89c5-6294bd241039
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12002.pdf 12002.pdf (424 Kb)
Publication type
Article
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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
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Thesis/dissertations
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