By Topic

Optimizing one-hidden layer neural network design using Evolutionary Programming

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
S. I. Sulaiman ; Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia ; T. K. Abdul Rahman ; I. Musirin

This paper presents the optimization of one-hidden layer artificial neural network (ANN) design using evolutionary programming (EP) for predicting the energy output of a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. In this study, the architecture and training parameters of the multi-layer feedforward back-propagation ANN model had been optimized while the prediction performance of the ANN was maximized. The proposed evolutionary programming-ANN (EPANN) model employs solar radiation and ambient temperature as its inputs while the sole output is the kilowatt-hour energy output produced from the grid connected PV system. The prediction performance was quantified using the average correlation coefficient and it was maximized by determining the optimum values for the number of nodes in the hidden layer, momentum rate and learning rate during an evolutionary training. Besides searching for the optimal number of nodes and optimal training parameters for each model, the highest correlation coefficient for the prediction required for the EPANN was investigated. It was found that the maximum average correlation coefficient obtained for the EPANN training is 0.9962. Besides that, the testing process produced sufficiently high correlation coefficient value of 0.9976.

Published in:

Signal Processing & Its Applications, 2009. CSPA 2009. 5th International Colloquium on

Date of Conference:

6-8 March 2009