Weighted Mamdani-type Fuzzy Inference System Based on Relative Ideal Preference System

Daud Mohamad, Fatin Liyana Mukhtar

Abstract


This paper presents a new method of determining weight of the fuzzy IF-THEN rules in a Fuzzy Inference System based on human intuition or expert judgment, known as the Relative Ideal Preference Scheme (RIPS). In the proposed scheme, an ideal preference rule is chosen from the given set of available fuzzy IF-THEN rules in the system which will be set with weight 1. The threshold weight and the interval between rule's weight are calculated prior to the computation of the weight of other rules. Rules are rearranged based on their level differences with respect to the ideal preferred rule. Finally, the weight of each rule is determined using the calculated threshold and interval between weights. An illustration of its implementation in a fuzzy inference system is presented with a numerical example. The proposed scheme has an advantage where the weights of the rules are determined systematically and simple.


Keywords


daud@tmsk.uitm.edu.my

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References


Anooj, P. K. (2012). Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University-Computer and Information Sciences, 24(1), 27-40.

Chen, S. M., & Huang, C. M. (2003). Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Transactions on Fuzzy Systems, 11(4), 495-506.

Chen, S-M, & Lin, H-L (2005), Generating weighted fuzzy rules for handling classification problems. International Journal of Electronic Business Management ,3(2),116-128.

Chen, Y. C., Wang, L. H., & Chen, S. M. (2006). Generating weighted fuzzy rules from training data for dealing with the Iris data classification problem. International Journal of Applied Science and Engineering, 4(1), 41-52.

Dubois, D. J. (1980). Fuzzy sets and systems: theory and applications (Vol. 144). Academic press.

He, X. (1988, October). Weighted fuzzy logic and its applications. In Computer Software and Applications Conference, 1988. COMPSAC 88. Proceedings., Twelfth International (pp. 485-489). IEEE.

Lau, P., & Chan, C. W. (1997). An enhanced weighted fuzzy reasoning algorithm for engineering applications. Computers & Mathematics with Applications, 34(1), 81-87.

López, R. S., & Macías, E. J. Selection of domotic systems by AHP based rules weights calculation on models of fuzzy rules. In International Conference on Renewable Energies and Power Quality, Santiago de Compostela, Spain (2012).

López, R. S., Macías, E. J., & de la Parte, M. P. Study and Comparison of Technologies in Home And Building Electronic Systems by Fuzzy Logic. In International conference on renewable energy and power quality (ICREPQ'08).

Meléndez, A., Castillo, O., Valdez, F., Soria, J., & Garcia, M. (2013). Optimal design of the fuzzy navigation system for a mobile robot using evolutionary algorithms. International Journal of Advanced Robotic Systems, 10(2), 139.

Olvera-García, M. Á., Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., & Hernández-Bautista, I. (2016). Air quality assessment using a weighted Fuzzy Inference System. Ecological informatics, 33, 57-74.

Paul, A. K., Shill, P. C., Rabin, M. R. I., & Murase, K. (2018). Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Applied Intelligence, 48(7), 1739-1756.

Ramakrishnan, R., & Rao, C. J. M. (1992). The fuzzy weighted additive rule. Fuzzy Sets and Systems, 46(2), 177-187.

Stoffel, K., Cotofrei, P., & Han, D. (2010, December). Fuzzy methods for forensic data analysis. In Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference of (pp. 23-28). IEEE.

Wang, X. Z., & Dong, C. R. (2009). Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy. IEEE Transactions on fuzzy systems, 17(3), 556-567.

Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.

Zimmermann, H.J. (2004). Fuzzy Set Theory and its Applications, 4th ed., Kluwer Academic Publishers, Boston.

Zolghadri, M. J., & Mansoori, E. G. (2007). Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis. Information Sciences, 177(11), 2296-2307.


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