A temporal neuro-fuzzy approach for time-series analysis

2003-09-08
Şişman Yılmaz, Arzu
Alpaslan, Ferda Nur
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 benchmark problem.
https://www.actapress.com/Abstract.aspx?paperId=15081

Suggestions

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 neurofuzzy model for rule-based systems
Alpaslan, Ferda Nur; Jain, L (1997-05-23)
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 Control System Architecture for Control of Non-Affine in Control, Open-Loop Unstable Underactuated Systems
Marangoz, Alp; Kutay, Ali Türker (2017-07-25)
In this paper, a control system architecture for control of non-affine in control, open-loop unstable underactuated system is discussed. Passivization of the unactuated (internal) system dynamics achieved through perturbation of trajectories of the actuated states, which are calculated through adaptive dynamic inversion technique, based on Tikhonov's theorem. Performance of the controller is shown through simulation of two open-loop unstable and locally uncontrollable example problems.
A Fuzzy logic based ensemble adaptive tile prefetching
Uluat, Mehmet Fatih; İşler, Veysi; Department of Computer Engineering (2014)
Prefetching is a process in which necessary portion of data is predicted and loaded into memory beforehand. The increasing usage of geographic data in different types of applications motivated the development of different prefetching techniques. These techniques are usually developed for specific type of applications such as 2D geographic information systems or 3D visualization applications and crafted for corresponding navigation patterns. However, as boundary between these application types blurs, these t...
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...
Citation Formats
A. Şişman Yılmaz and F. N. Alpaslan, “A temporal neuro-fuzzy approach for time-series analysis,” Benalmadena, Spain, 2003, p. 679, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87604.