Determining significant parameters in the design of ANFIS

Author

Alizadeh, Meysam ; Lewis, Michael ; Zarandi, Mohammad Hossein Fazel ; Jolai, Fariborz

Author_Institution

Sch. of Inf. Sci., Univ. of Pittsburgh, Pittsburgh, PA, USA

fYear

2011

fDate

18-20 March 2011

Firstpage

1

Lastpage

6

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) has become a popular tool in neuro-fuzzy modeling. However, since it includes many parameters needed to be set, its designing process is a complicated and time-intensive task for experimenters. To tackle this problem, in this paper we implement the Design of Experiment (DOE) technique to identify the significant parameters of ANFIS when it applies to the problem of stock price prediction. Using full factorial design, nine factors are considered as independent variables. Results identify six factors as statistically significant parameters, as well as four significant interactions between some independent variables.

Keywords

design of experiments; fuzzy reasoning; neural nets; pricing; stock markets; ANFIS design; adaptive neurofuzzy inference system; design of experiment technique; full factorial design; neurofuzzy modeling; stock price prediction; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Data models; Indexes; Shape; Training; ANFIS; Design of Experiment; Neuro-fuzzy systems; Stock price prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American

Conference_Location

El Paso, TX

ISSN

Pending

Print_ISBN

978-1-61284-968-3

Electronic_ISBN

Pending

Type

conf

DOI

10.1109/NAFIPS.2011.5751958

Filename

5751958