Multilayer feedforward networks are universal approximators
Neural Networks
Multiple permeability predictions using an observational learning algorithm
Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
A committee machine with empirical formulas for permeability prediction
Computers & Geosciences
Expert Systems with Applications: An International Journal
Hybrid PSO and GA for neural network evolutionary in monthly rainfall forecasting
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Hybird evolutionary algorithms for artificial neural network training in rainfall forecasting
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Hi-index | 12.05 |
In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm-neural network strategy (GA-ANN). The proposed algorithm combines the local searching ability of the gradient-based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA-ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neural network outperforms the gradient descent-based neural network.