Artificial Neural Networks (ANNs) must be able to learn by experience from environment. Some neural networks are capable of approximating not only deterministic (non-random) functions but also stochastic processes. These networks, named Stochastic Neural Networks represents a generalisation of the usually defined neural networks. From an application point of view such a class of networks is more adherent to the real world whose nature is essentially stochastic. In this paper an original recursive training algorithm for the approximation of a large class of stochastic process has been presented. The methodology is based on the canonical representation of non-stationary stochastic processes by means of Brownian motion processes.

Neural network approximation of stochastic processes: A recursive algorithm / Crippa, Paolo; Turchetti, Claudio. - STAMPA. - 82:(2002), pp. 1316-1320.

Neural network approximation of stochastic processes: A recursive algorithm

CRIPPA, Paolo;TURCHETTI, Claudio
2002-01-01

Abstract

Artificial Neural Networks (ANNs) must be able to learn by experience from environment. Some neural networks are capable of approximating not only deterministic (non-random) functions but also stochastic processes. These networks, named Stochastic Neural Networks represents a generalisation of the usually defined neural networks. From an application point of view such a class of networks is more adherent to the real world whose nature is essentially stochastic. In this paper an original recursive training algorithm for the approximation of a large class of stochastic process has been presented. The methodology is based on the canonical representation of non-stationary stochastic processes by means of Brownian motion processes.
2002
Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies (KES 2002) - Frontiers in Artificial Intelligence and Applications, Part 2
9781586032807
1586032801
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/42842
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