Evaluation of inconsistency in a 2-way fuzzy adaptive system using shadowed sets

Date

2001-05-24

Author

Gurkan, E
Erkmen, Aydan Müşerref
Erkmen, İsmet

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

228
views

0
downloads

Our objective in this paper is to evaluate inconsistency for our proposed 2-way fuzzy adaptive system that makes use of intuitionistic fuzzy sets. Uncertainty is modeled as the width of the interval introduced by the independent assignment of membership and nonmembership functions of the intuitionistic fuzzy sets. There is only a consistency constraint in this assignment, violation of which gives rise to inconsistency in the system. The inconsistency model using this fact is reduced through training. There are two phases of training for our proposed 2-way adaptive fuzzy system. The first phase is to reduce inconsistency introduced by the inconsistent assignment of membership and nonmembership functions. The resultant system is a consistent 2-way fuzzy adaptive system. The evaluation of the degree of reduction of inconsistency is carried out at the end of phase I training by forming the shadowed set patterns of the membership and nonmembership functions after training. The shadowed set patterns are first mapped into types of inconsistencies which are further classified according to the global index of fuzziness generated out of the output membership and nonmembership functions. It is seen that the system is able to reduce inconsistency very efficiently.

Subject Keywords

2-way adaptive fuzzy systems, Types of inconsistency, Uncertainty, Shadowed sets, İntuitionistic fuzzy sets

URI

https://hdl.handle.net/11511/45922

DOI

https://doi.org/10.1109/ismvl.2001.924562

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Reducing inconsistencies in intuitionistic 2-way adaptive fuzzy control systems Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (2000-08-30) Our objective in this paper is to model and reduce inconsistency in expert knowledge for our proposed 2-way adaptive fuzzy system that makes use of intuitionistic fuzzy sets. Intuitionistic fuzzy sets model an interval valued distribution of information in the adaptive control architecture with the necessity at the lower bound as the degree of membership functions and the possibility at the upper bound as the complement of the degree of nonmembership functions. Uncertainty is modelled as the width of this i...
Two-way fuzzy adaptive identification and control of a flexible-joint robot arm Gurkan, E; Erkmen, İsmet; Erkmen, Aydan Müşerref (2002-08-01) The objective in this paper is to apply our proposed two-way fuzzy adaptive system that makes use of intuitionistic fuzzy sets to the identification and model-based control of a flexible-joint robot arm. Uncertainty and inconsistency are modelled in the proposed system such as uncertainty is the width of the interval introduced by the independent assignment of membership and nonmembership functions of the intuitionistic fuzzy sets; and inconsistency is the violation of the consistency inequality in this ass...
Intuitionistic, 2-way adaptive fuzzy control Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (1999-05-15) Our objective in this paper is to develop a 2-way adaptive fuzzy control system that makes use of the intuitionistic fuzzy sets for modeling expert knowledge bearing uncertainty. Adaptive fuzzy control systems are fuzzy logic systems whose rule parameters are automatically adjusted through training. The training of such system was applied until now, to supports of rule propositions with single distribution such that they can be termed 1-way adaptive. In our system, all supports to propositions have interval...
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...
Genetically tuned fuzzy scheduling for flexible manufacturing systems. Erkmen, Aydan Müşerref; Anlagan, O; Unver, O (1997-04-25) This paper focuses on the development and implementation of a Genetically Tuned Fuzzy Scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on time criticality in deadline and machine need, taking into account machine availability, uniformity, process time and sel...

Citation Formats

E. Gurkan, A. M. Erkmen, and İ. Erkmen, “Evaluation of inconsistency in a 2-way fuzzy adaptive system using shadowed sets,” 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45922.