Ensemble model of Artificial Neural Networks with randomized number of hidden neurons

• Main Authors: Fatai Adesina, Anifowose, Jane, Labadin
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://ir.unimas.my/id/eprint/8474/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf; http://ir.unimas.my/id/eprint/8474/; http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6637562
• Institution: Universiti Malaysia Sarawak

Conventional artificial intelligence techniques and their hybrid models are incapable of handling several hypotheses at a time. The limitation in the performance of certain techniques has made the ensemble learning paradigm a desirable alternative for better predictions. The petroleum industry stands to gain immensely from this learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved hydrocarbon exploration, production, and management activities. Artificial Neural Networks (ANN) has been applied in petroleum engineering but widely reported to be lacking in global optima caused mainly by the great challenge involved in the determination of optimal number of hidden neurons. This paper presents a novel ensemble model of ANN that uses a randomized algorithm to generate the number of hidden neurons in the prediction of petroleum reservoir properties. Ten base learners of the ANN model were created with each using a randomly generated number of hidden neurons. Each learner contributed in solving the problem and a single ensemble solution was evolved. The performance of the ensemble model was evaluated using standard evaluation criteria. The results showed that the performance of the proposed ensemble model is better than the average performance of the individual base learners. This study is a successful proof of concept of randomization of the number of hidden neurons and demonstrated the great potential for the application of this learning paradigm in petroleum reservoir characterization.