International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.8, No.2, Feb. 2016
Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights
Full Text (PDF, 750KB), PP.1-12
Defuzzification converts the final fuzzy output set of fuzzy controller and fuzzy inference systems to a significant crisp value. However, there are various mathematical methods for defuzzification, but there is not any certain systematic method for choosing the best strategy. In this paper, first we explain the structure of a fuzzy inference system and then after a short review of defuzzification criteria and properties, the main classification groups of most widely used defuzzification methods are presented. In the following after discussing some existing techniques, two new defuzzification methods are proposed by presenting their general performance and computational formulas. However, the principle of these two methods is using weights associated with output fuzzy set like WFM or QM, but unlike the existing approaches, they consider the final aggregated consequent and implicated functions simultaneously to calculate the weights. To show how the proposed methods act, two numerical examples are solved using the presented methods and the results are compared with some of common defuzzification techniques.
Cite This Paper
Amin Amini, Navid Nikraz,"Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.2, pp.1-12, 2016. DOI: 10.5815/ijisa.2016.02.01
Saade, J. J., Diab, H. B., Defuzzification Methods and New Techniques for Fuzzy Controllers, Iranian Journal of Electrical and Computer Engineering, Vol. 3, No. 2, (2004), pp. 161–174.
Mukherjee, S., Bhattacharyya, R., Kar, S., A Novel Defuzzification Method Based on Fuzzy Distance, International Journal of Fuzzy Systems and Rough Systems (IJFSRS),Vol. 3, No. 1, (Jan-Jun 2010).
Zhang, H., Liu, D., Fuzzy Modeling and Fuzzy Control, Birkhauser, Boston (2006), p. 16.
Timothy, J. R., Parkinson, W.J., Fuzzy Logic and Probability Applications, 2002, Chapter 2, pp. 29-43.
Zadeh, L.A., Fuzzy logic and approximate reasoning, Synthese, 30 (1975), pp. 407–428.
Kosko, B., Fuzzy systems as universal approximators, IEEE Transactions on Computers, Vol. 43, (1994), pp. 1329-1333.
R. Jager, Fuzzy logic in control, Ph.D. dissertation, Tech. Univ. Delft, Delft, The Netherlands, (1995).
Saletic, D. Z., Velasevic, D. M., Mastorakis, N. E., Analysis of Basic Defuzzification Techniques, Proceedings of the 6th WSES International Circuits, Systems, Communications and Computers, CSCC 2002, Rethymnon, Greece, (July 2002).
Siahbazi, A., Barzegar, A., Vosoogh, M., Mirshekaran, A. M., Soltani, S.,Design Modified Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Spherical Motor, IJISA, ,vol.6, no.3, (2014), pp.12-25.
Shankar Anand, M., Tyagi, Ba., Design and Implementation of Fuzzy Controller on FPGA, IJISA, vol.4, (2012), no.10, pp.35-42.
A. Aly, A., S. Abo El-Lail, A., A. Shoush, K., A. Salem, F., Intelligent PI Fuzzy Control of An Electro-Hydraulic Manipulator, (2012), IJISA, vol. 4, no. 7, pp. 43-49,.
Allam, F., Nossair, Z., Gomma, H., Ibrahim, I., Abdelsalam, M., Evaluation of Using a Recurrent Neural Network (RNN) and a Fuzzy Logic Controller (FLC) In Closed Loop System to Regulate Blood Glucose for Type-1 Diabetic Patients, IJISA, vol.4, no.10, (2012), pp.58-71,.
Runkler, T. A., Extended Defuzzification Methods and their Properties, IEEE Transactions, (1996), pp. 694-700.
Leekwijck, W. V., Kerre, E. E., Defuzzification: criteria and classification, Fuzzy Sets and Systems, Vol. 108 (1999), pp. 159-178.
Runkler, T. A., Glesner, M., A Set of Axioms for Defuzzification Strategies Towards a Theory of Rational Defuzzification Operators, Proceedings of the IEEE International Conference on Fuzzy System, (1993), pp. 1161-1166.