Multilayer feedforward networks are universal approximators
Neural Networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
Regularization theory and neural networks architectures
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
On quantitative a priori measures of identifiability of coefficients of linear dynamic systems
Journal of Computer and Systems Sciences International
Neural networks committee for improvement of metal's mechanical properties estimates
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Optical Memory and Neural Networks
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Neural network modeling of vector multivariable functions in ill-posed approximation problems
Journal of Computer and Systems Sciences International
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In this paper a multidimensional function approximation problem is stated. This problem is characterized by the strong influence of arguments measurement errors on the accuracy of the function estimation and a small amount of train data. A neural networks based solution is used for this problem. To improve the accuracy of the approximation model it is proposed to use a neural networks committee with an original decision making rule for the construction of the generalized function estimate. The developed rule is based on a specially introduced indirect accuracy measure and @a-quantile calculation of its probability level. The decision making rule is trained on a set of patterns and uses statistical properties of each pattern's processing by the committee's networks. The computational scheme of the proposed approximation model and the effectiveness of the proposed approach are demonstrated on a simple model example. The developed method was successfully applied to a real industrial problem of metal's hardness characteristics estimation on the basis of kinetic indentation data. The results of modeling experiments are discussed.