by Rafał Scherer, Agata Pokropińska
Abstract:
An application of neuro-fuzzy systems to supporting trading decisions is presented. The system has the ability to use expert knowledge and to be fitted to the learning data by various machine learning techniques. The proposed approach uses the backpropagation algorithm to determine system parameters on the basis of several indices. Experiments were made on past data showing relatively good performance of the proposed approach.
Reference:
R. Scherer, A. Pokropińska, "Financial Prediction with Neuro-fuzzy Systems", Lecture Notes in Artificial Intelligence, vol. 5097, 2008, pp. 1120-1126.
Bibtex Entry:
@ARTICLE{SchPokropICAISC2008,
author = {Rafał Scherer and Agata Pokropińska},
title = {Financial Prediction with Neuro-fuzzy Systems},
journal = {Lecture Notes in Artificial Intelligence},
year = {2008},
volume = {5097},
pages = {1120-1126},
abstract = {An application of neuro-fuzzy systems to supporting trading decisions
is presented. The system has the ability to use expert knowledge
and to be fitted to the learning data by various machine learning
techniques. The proposed approach uses the backpropagation algorithm
to determine system parameters on the basis of several indices. Experiments
were made on past data showing relatively good performance of the
proposed approach.},
url = {http://www.springerlink.com/content/b716x592314w4ujr/}
}