Multilayer feedforward networks are universal approximators
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
An approximation by neural networkswith a fixed weight
Computers & Mathematics with Applications
On the approximation of stochastic processes by approximate identity neural networks
IEEE Transactions on Neural Networks
Double approximate identity neural networks universal approximation in real lebesgue spaces
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Universal approximation capability of feedforward neural networks with one hidden layer states that these networks are dense in the space of functions. In this paper, the concept of the Mellin approximate identity functions is proposed. By using this concept, It is shown that feedforward Mellin approximate identity neural networks with one hidden layer can approximate any positive real continuous function to any degree of accuracy. Moreover, universal approximation capability of these networks is extended to positive real Lebesgue spaces.