Fuzzy Logic Applied to Inflation Control in the Nigerian Economy
Machine Learning Research
Volume 3, Issue 4, December 2018, Pages: 69-72
Received: Mar. 9, 2019;
Accepted: Apr. 22, 2019;
Published: May 23, 2019
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Ibrahim Goni, Department Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria
Mohammed Alhaji Maunde Usman, Department of Economics, Faculty of Social and Management Science, Adamawa State University, Mubi, Nigeria
Auwal Nata’ala, Department of Computer Science, School of Information Technology Federal Polytechnic, Kaura Namoda, Zamfara State, Nigeria
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In this research work, a fuzzy logic system for inflation control in Nigerian economy is presented. The system consists of four (4) major components which include; the Knowledge base, the Fuzzification, the Inference engine and Defuzzification. Knowledge base were developed based on the discussion with the domain expert and observations of the Nigerian economy. Mamdani's fuzzy inference engine were used to infer data from the rules developed. This resulted in the establishment of some degrees of membership functions of input variables on the output. The methodology allows for High, Low, Yes and No to be applied in order to get the required result. Gaussian membership function was employed to evaluate the degree of participation of each input parameter and the defuzzification technique used in this work is Centriod of Area. Fuzzy logic system has been developed as an alternative to the traditional methods, in order to control inflation in the Nigerian economy.
Fuzzy Logic, Inflation, Defuzzification, Fuzzification, Knowledge Base, Mamdani
To cite this article
Mohammed Alhaji Maunde Usman,
Fuzzy Logic Applied to Inflation Control in the Nigerian Economy, Machine Learning Research.
Vol. 3, No. 4,
2018, pp. 69-72.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Victor OA. The Causes of Persistent Inflation in Nigeria. CBN Journal of Applied Statistics, 2016; 7 (2).
Eskey 10 causes of inflation in Nigeria, Information guide in Nigeria, 2018 available online at https: //infoguidenigeria.com/causes-inflation-nigeria/
Marcus F. (2011). The Application of Fuzzy Logic in Determining Linguistic Rules and Associative Membership Functions for the Control of a Manufacturing Process, M. Engr. Dissertation Dublin Institute of Technology India.
Ponce-Cruz, FD. Ramirez-Figueroa. Intelligent Control Systems with LabVIEW™ Springer 2010.
Kalaichelvi A, Malini, PH, Application of fuzzy soft sets to investment decision making problem, International Journal of Mathematical Sciences and Applications 2011; 1(3), 1583-1586.
Karaca F. Taş, V. Decision making problem for life and non-life insurances, Journal of BAUN Inst. Sci. Technol. 2018; 20 (1), 572-588.
Özgür NY., Taş N., A note on "application of fuzzy soft sets to investment decision making problem", Journal of New Theory, 2015; 7 1-10.
Taş, N., Özgür NY., Demir, P. An application of soft set and fuzzy soft set theories to stock management, Süleyman Demirel University Journal of Natural and Applied Sciences 2017; 21 (2), 791-196.
Vincenzo D. G., Pierfrancesco D. P. and Giovanni B. C. (2017) Valuation of Real Estate Investments through. Fuzzy Logic. Buildings MDPI.
Jagendra D. and Ramesh T. (2015) Design of Mamdani - Type Model for Predicting the Future Price of Fuel on theBasis of Demand and Supply International Journal on Recent and Innovation Trends in Computing and Communication 3 (6). 3667-3671.