A temporal neurofuzzy model for rule-based systems

Date

1997-05-23

Author

Alpaslan, Ferda Nur
Jain, L

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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.

Subject Keywords

Artificial neural networks, Backpropagation algorithm, Temporal information, Fuzzy logic

URI

https://hdl.handle.net/11511/53886

Collections

Department of Computer Engineering, Conference / Seminar

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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 ...
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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.