Kolmogorov's theorem and multilayer neural networks
Neural Networks
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Industrial Applications of Neural Networks
Industrial Applications of Neural Networks
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Evolutionary Design of MLP Neural Network Architectures
SBRN '97 Proceedings of the 4th Brazilian Symposium on Neural Networks (SBRN '97)
An Algorithm for Automatic Design of Two Hidden Layered Artificial Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Structure optimization of neural networks for evolutionary design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Design of MLP using Evolutionary Strategy with Variable Length Chromosomes
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Design of neural networks for fast convergence and accuracy: dynamics and control
IEEE Transactions on Neural Networks
Sensitivity analysis of multilayer perceptron to input and weight perturbations
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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The neural representation of a physical process has the objective of explaining the cause-effect relationship among the parameters involved in the process. The representation is normally evaluated through the error reached during the training and validation processes. As the neural representation is not based on the physical principles, its mathematical representation can be correct in the quantitative aspect but not in the qualitative one. In this work, it is shown that a neural representation can fail when its qualitative aspect is evaluated. The search of the ideal neurons quantity for the hidden layer of the MLP neural network, by means of Genetic Algorithms and the sensitivity factors calculated directly from the neural networks during the training process, is presented. The new optimization structure has the objective to find a neural network structure capable to represent the process quantitatively and qualitatively. The sensitivity factors, when compared with the expert knowledge of the human agent, represented through symbolic rules, can evaluate not only the quantitative but also the qualitative aspect of the process being represented through a specific neural structure. The results obtained, and the time (epochs) necessaries to reach the neural network target show that this combination is promising. As a case study, the new structure is applied for the cold rolling process.