DocumentCode :
2559465
Title :
The application of fuzzy neural networks in stock price forecasting based On Genetic Algorithm discovering fuzzy rules
Author :
Yang, Kongyu ; Wu, Min ; Lin, Jihui
Author_Institution :
Sch. of Inf. Manage., Beijing Inf. Sci. & Technol. Univ., Beijing, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
470
Lastpage :
474
Abstract :
This paper proposes some methods to improve black-box model considering problems existed in its application. The improvement is achieved mainly by applying GA (Genetic Algorithm) in fuzzy systems to discover rules, eliminate errors or invalid rules caused by noisy data, and thus form valid sets of rules. Evaluation of the rule sets, as that of the whole prediction model, is performed through known knowledge and theories. At last, fuzzy reasoning approach is used based on the rule sets to predict price trend of stock market.
Keywords :
economic forecasting; fuzzy neural nets; fuzzy reasoning; genetic algorithms; pricing; stock markets; black-box model; fuzzy neural networks; fuzzy reasoning approach; fuzzy rules; fuzzy systems; genetic algorithm; stock market price trend; stock price forecasting; Fluctuations; Fuzzy reasoning; Fuzzy systems; Genetic algorithms; Input variables; Predictive models; Stock markets; Fuzzy rules; Genetic algorithm; Prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
ISSN :
2157-9555
Print_ISBN :
978-1-4577-2130-4
Type :
conf
DOI :
10.1109/ICNC.2012.6234684
Filename :
6234684
Link To Document :
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