A temporal neurofuzzy model for rule-based systems

This paper reports the development of a temporal neuro-fuzzy model using fuzzy reasoning which is capable of representing the temporal information. The system is implemented as a feedforward multilayer neural network. The learning algorithm is a modification of the backpropagation algorithm. The system is aimed to be used in medical diagnosis systems.


A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
A temporal neuro-fuzzy approach for time-series analysis
Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003)
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 ...
A temporal neuro-fuzzy approach for time-series analysis
Şişman Yılmaz, Arzu; Alpaslan, Ferda Nur (null; 2003-09-08)
In this paper, a temporal neuro-fuzzy system is presented which provides an environment that keeps temporal rela tionships between input and output variables. The sys tem is used to forecast the future behavior of time series data. It is based on ANFIS neuro-fuzzy system and named ANFIS unfolded in time. The rule base contains tempo ral TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, a modified back-propagation learning algorithm is used. The model is tested on Gas-furnace data which is a benchm...
A complete axiomatization for fuzzy functional and multivalued dependencies in fuzzy database relations
Sozat, MI; Yazıcı, Adnan (Elsevier BV, 2001-01-15)
This paper first introduces the formal definitions of fuzzy functional and multivalued dependencies which are given on the basis of the conformance values presented here. Second, the inference rules are listed after both fuzzy functional and multivalued dependencies are shown to be consistent, that is, they reduce to those of the classic functional and multivalued dependencies when crisp attributes are involved. Finally, the inference rules presented here are shown to be sound and complete for the family of...
Representing temporal knowledge in connectionist expert systems
Alpaslan, Ferda Nur (1996-09-27)
This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Citation Formats
F. N. Alpaslan and L. Jain, “A temporal neurofuzzy model for rule-based systems,” 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53886.