Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Reserve estimation using neural network techniques
Computers & Geosciences
Radial basis function and related models: an overview
Signal Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Alternative Neural Network Training Methods
IEEE Expert: Intelligent Systems and Their Applications
Fast learning in networks of locally-tuned processing units
Neural Computation
IEEE Transactions on Signal Processing
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
WSEAS Transactions on Systems and Control
LavaNet-Neural network development environment in a general mine planning package
Computers & Geosciences
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This paper presents a study highlighting the predictive performance of a radial basis function (RBF) network in estimating the grade of an offshore placer gold deposit. In applying the radial basis function network to grade estimation of the deposit, several pertinent issues regarding RBF model construction are addressed in this study. One of the issues is the selection of the RBF network along with its center and width parameters. Selection was done by an evolutionary algorithm that utilizes the concept of cooperative coevolutions of the RBFs and the associated network. Furthermore, the problem of data division, which arose during the creation of the training, calibration and validation of data sets for the RBF model development, was resolved with the help of an integrated approach of data segmentation and genetic algorithms (GA). A simulation study conducted showed that nearly 27% of the time, a bad data division would result if random data divisions were adopted in this study. In addition, the efficacy of the RBF network was tested against a feed-forward network and geostatistical techniques. The outcome of this comparative study indicated that the RBF model performed decisively better than the feed-forward network and the ordinary kriging (OK).