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Ashraf Osman Ibrahim
Universiti Teknologi Malaysia

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Artificial Neural Network Weight Optimization: A Review Abdirashid Salad Nur; Nor Haizan Mohd Radzi; Ashraf Osman Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 9: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i9.pp6897-6902

Abstract

Optimizing the weights of Artificial Neural Networks (ANNs) is a great important of a complex task in the research of machine learning due to dependence of its performance to the success of learning process and the training method. This paper reviews the implementation of meta-heuristic algorithms in ANNs’ weight optimization by studying their advantages and disadvantages giving consideration to some meta-heuristic members such as Genetic algorithim, Particle Swarm Optimization and recently introduced meta-heuristic algorithm called Harmony Search Algorithm (HSA). Also, the application of local search based algorithms to optimize the ANNs weights and their benefits as well as their limitations are briefly elaborated. Finally, a comparison between local search methods and global optimization methods is carried out to speculate the trends in the progresses of ANNs’ weight optimization in the current resrearch.
Co-Authors Abdirashid Salad Nur Nor Haizan Mohd Radzi