AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Sigmoidal Function Classes for Feedforward Artificial Neural Networks
Neural Processing Letters
Journal of Intelligent and Robotic Systems
Neural network based tire/road friction force estimation
Engineering Applications of Artificial Intelligence
Uniform Approximation Capabilities of Sum-of-Product and Sigma-Pi-Sigma Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions
Information Sciences: an International Journal
Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem
Robotics and Computer-Integrated Manufacturing
Learning of geometric mean neuron model using resilient propagation algorithm
Expert Systems with Applications: An International Journal
The errors of approximation for feedforward neural networks in the Lp metric
Mathematical and Computer Modelling: An International Journal
Approximation of level continuous fuzzy-valued functions by multilayer regular fuzzy neural networks
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
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In this paper, we investigate the capability of approximating functions in C(R¯n) by three-layered neural networks with sigmoidal function in the hidden layer. It is found that the boundedness condition on the sigmoidal function plays an essential role in the approximation, as contrast to continuity or monotonity condition. We point out that in order to prove the neural network in the n-dimensional case, all one needs to do is to prove the case for one dimension. The approximation in Lp-norm (1