An approach to online optimization of heuristic coordination algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Approximation of stochastic processes by T--S fuzzy systems
Fuzzy Sets and Systems
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
The universal approximation capabilities of mellin approximate identity neural networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The ability of a neural network to learn from experience can be viewed as closely related to its approximating properties. By assuming that environment is essentially stochastic it follows that neural networks should be able to approximate stochastic processes. The aim of this paper is to show that some classes of artificial neural networks exist such that they are capable of providing the approximation, in the mean square sense, of prescribed stochastic processes with arbitrary accuracy. The networks so defined constitute a new model for neural processing and extend previous results concerning approximating capabilities of artificial neural networks