The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Advances in Engineering Software
Nonlinear system identification: From multiple-model networks to Gaussian processes
Engineering Applications of Artificial Intelligence
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
A fast multi-output RBF neural network construction method
Neurocomputing
Engineering Applications of Artificial Intelligence
Training RBF neural network via quantum-behaved particle swarm optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Adaptive training of radial basis function networks using particle swarm optimization algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Using a mahalanobis-like distance to train radial basis neural networks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
IEEE Transactions on Neural Networks
Genetic evolution of radial basis function coverage using orthogonal niches
IEEE Transactions on Neural Networks
Statistical inference in a redesigned Radial Basis Function neural network
Engineering Applications of Artificial Intelligence
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The Hybrid Learning Process method proposed in this work, is applied to a Genetic Algorithm and Mahalanobis distance, instead of computing the centers matrix by Genetic Algorithm. It is determined in such a way as to maximize the coefficient of determination R^2 and the Fitness Function depends on the prediction accuracy fitted by the Hybrid Learning approach, where the coefficient of determination R^2 is a global metric evaluation. The Mahalanobis distance is a measurement of distance which uses the correlation between variables and takes into account the covariance and variance matrix in the input variables; this distance helps to reduce the variance into variables. The purpose of this work is to show a methodology to modify the Radial Basis Function and also improve the parameters and variables that are associated with Radial Basis Function learning processes; since the Radial Basis Function has mainly two problems, the Euclidean distance and the calculation of centroids. The results indicated that the statistical methods such as Residual Analysis are good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The principal conclusion of this work is that the Radial Basis Function Redesigned improved the accuracy of the model using a Hybrid Learning Process and the Radial Basis showed very good performance in a real case, considering the prediction of specific responses in a laser welding process.