Neuro-fuzzy networks in time series modelling

Author

Gorzalczany, M.B. ; Gluszek, Adam

Author_Institution

Dept. of Electr. & Comput. Eng., Kielce Univ. of Technol., Poland

Volume

1

fYear

2000

fDate

2000

Firstpage

450

Abstract

The paper briefly presents and compares four neuro-fuzzy systems used for rule-based modelling of dynamic processes (chaotic Mackey-Glass time series). The following systems have been considered: nfMod, the system proposed in this paper; the well-known ANFIS and NFIDENT systems; and an alternative neuro-fuzzy system reported in literature. The main criterion of comparison of all systems is their performance (the accuracy of modelling) versus interpretability (the transparency and the ability to explain generated decisions; it also includes an analysis and pruning of obtained fuzzy-rule bases)

Keywords

fuzzy neural nets; time series; ANFIS; NFIDENT; accuracy of modelling; chaotic Mackey-Glass time series; dynamic processes; fuzzy-rule bases; interpretability; neuro-fuzzy systems; performance; pruning; rule-based modelling; time series modelling; transparency; Artificial intelligence; Artificial neural networks; Chaos; Fusion power generation; Fuzzy neural networks; Fuzzy systems; Intelligent networks; Network synthesis; Paper technology; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885853

Filename

885853