Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Neural network design
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
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
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Advances in Engineering Software
Nonlinear system identification: From multiple-model networks to Gaussian processes
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
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Neural Networks (NNs) have been widely used in many industrial processes for prediction and optimization and they have been proven to be useful tools for explaining complex processes. The main objective of this work consists of improving the accuracy of a Radial Basis Function Neural Network Redesigned by Genetic Algorithm and Mahalanobis distance for predicting a welding process. The evaluation function in this approach considers the use of the Coefficient of Determination R2. The results indicated that the statistical method R2 is a good alternative to validate the efficiency of the Neural Network model. The principal conclusion in this work is that the Radial Basis Function Redesigned by Genetic Algorithm and Mahalanobis distance had a very good performance in a real case, considering the prediction of specific responses in a welding process.