The subject of this thesis is to develop a temporal neuro-fuzzy system for fore-
casting the future behavior of a multivariate time series data.
The system has two components combined by means of a system interface.
First, a rule extraction method is designed which is named Fuzzy MAR (Multivari-
ate Auto-regression). The method produces the temporal relationships between
each of the variables and past values of all variables in the multivariate time series
system in the form of fuzzy rules. These rules may constitute the rule-base in a
fuzzy expert system.
Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in -
time is designed in order to make the use of fuzzy rules, to provide an environment
that keeps temporal relationships between the variables and to forecast the future
behavior of data. The rule base of ANFIS unfolded in time contains temporal
TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation
learning algorithm is used. The system takes the multivariate data and the num-
ber of lags needed which are the output of Fuzzy MAR in order to describe a
variable and predicts the future behavior.
Computer simulations are performed by using synthetic and real multivariate
data and a benchmark problem (Gas Furnace Data) used in comparing neuro-
fuzzy systems. The tests are performed in order to show how the system efficiently
model and forecast the multivariate temporal data. Experimental results show
that the proposed model achieves online learning and prediction on temporal data.
The results are compared by other neuro-fuzzy systems, specifically ANFIS.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/570366/index.pdf |
| Date | 01 January 2003 |
| Creators | Sisman Yilmaz, Nuran Arzu |
| Contributors | Alpaslan, Ferda Nur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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