Mlps (mono layer polynomials and multi layer perceptrons) for nonlinear modeling
The Journal of Machine Learning Research
On-line fuzzy modeling via clustering and support vector machines
Information Sciences: an International Journal
The clustering algorithm for nonlinear system identification
WSEAS Transactions on Computers
Combined input variable selection and model complexity control for nonlinear regression
Pattern Recognition Letters
On-Line Modeling Via Fuzzy Support Vector Machines
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Online fuzzy modeling with structure and parameter learning
Expert Systems with Applications: An International Journal
Clustering for nonlinear system identification
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
A dynamic artificial neural network model for forecasting nonlinear processes
Computers and Industrial Engineering
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
Multiple fuzzy neural networks modeling with sparse data
Neurocomputing
Multilayer perceptron for simulation models reduction: Application to a sawmill workshop
Engineering Applications of Artificial Intelligence
Advances in Artificial Neural Systems
On-line modeling via fuzzy support vector machines and neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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We study how statistical tools which are commonly used independently can advantageously be exploited together in order to improve neural network estimation and selection in nonlinear static modeling. The tools we consider are the analysis of the numerical conditioning of the neural network candidates, statistical hypothesis tests, and cross validation. We present and analyze each of these tools in order to justify at what stage of a construction and selection procedure they can be most useful. On the basis of this analysis, we then propose a novel and systematic construction and selection procedure for neural modeling. We finally illustrate its efficiency through large-scale simulations experiments and real-world modeling problems.