Artificial Neural Networks as Approximators of Stochastic Processes

Autor:	M. R. Belli, Massimo Conti, Claudio Turchetti, Simone Orcioni, Paolo Crippa
Rok vydání:	1998
Předmět:	Quantitative Biology::Neurons and Cognition Neuro-fuzzy Artificial neural network Computer science Stochastic process business.industry Artificial intelligence Stochastic neural network business
Zdroj:	ICANN 98 ISBN: 9783540762638
DOI:	10.1007/978-1-4471-1599-1_95
Popis:	Artificial (or biological) Neural Networks must be able to form by learning internal memory of the environment to determine decisions and subsequent actions to stimuli. By assuming that environment is essentially stochastic it follows that the mathematical framework for learning information from environment is the theory of stochastic processes approximation. The aim of this paper is to show that classes of neural networks capable of approximating stochastic processes exist.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6144c9b2dbcc19005d3bc03b666935c5 https://doi.org/10.1007/978-1-4471-1599-1_95 Zobrazit plný text záznamu