Towards an improved ensemble learning model of artificial neural networks: Lessons learned on using randomized numbers of hidden neurons

Artificial Neural Networks (ANN) have been widely applied in petroleum reservoir characterization.Despite their wide use, they are very unstable in terms of performance. Ensemble machine learning iscapable of improving the performance of such unstable techniques. One of the challenges of using ANNis choosing the appropriate number of hidden neurons. Previous studies have proposed ANN ensemblemodels with a maximum of 50 hidden neurons in the search space thereby leaving rooms for furtherimprovement. This chapter presents extended versions of those studies with increased search spaces usinga linear search and randomized assignment of the number of hidden neurons. Using standard modelevaluation criteria and novel ensemble combination rules, the results of this study suggest that having alarge number of "unbiased" randomized guesses of the number of hidden neurons beyond 50 performsbetter than very few occurrences of those that were optimally determined.