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- W2799343306 abstract "This paper proposes a novel method to select hidden neurons in ELMAN neural networks for windspeed prediction application. Either over fitting or under fitting problem caused due to the random choice ofhidden neuron numbers in artificial neural network. This paper suggests the solution to solve either over fittingor under fitting problems. In order to select proper hidden neuron numbers, 75 different criteria tested by themeans of statistical errors. The simulation results proved that proposed approach improves the accuracy andreduce the error to the least. The perfect building of ELMAN network with five inputs using fixation criteria isvalidated based on convergence theorem. To evaluate the performance of the proposed approach simulationwere performed on real time wind data. Comparative analysis has performed to select the hidden neuronnumbers in neural networks. The presented approach is very simple, with the least error, and more effective toselect the amount of hidden neurons in ELMAN neural network." @default.
- W2799343306 created "2018-05-17" @default.
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- W2799343306 date "2018-01-01" @default.
- W2799343306 modified "2023-09-24" @default.
- W2799343306 title "A Novel Method to Select Hidden Neurons in ELMAN Neural Network for Wind Speed Prediction Application" @default.
- W2799343306 hasPublicationYear "2018" @default.
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