A Comprehensive Study on Fuzzy Inference System and its Application in the field of Engineering

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-54 Number-1
Year of Publication : 2017
Authors : Sheena A D, M. Ramalingam, B. Anuradha
DOI :  10.14445/22315381/IJETT-V54P206


Sheena A D, M. Ramalingam, B. Anuradha "A Comprehensive Study on Fuzzy Inference System and its Application in the field of Engineering", International Journal of Engineering Trends and Technology (IJETT), V54(1),36-40 December 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

This paper presents a study report on Fuzzy Control and Fuzzy Inference System (FIS) highlighting the application for Engineering projects. Based on the project focus, FIS model is very much helpful to identify a project performance evaluation. The reasoning processes of a structure of fuzzy rules, the knowledge base of the system is excellent. By applying this, with reasoning it is practical to have a quantitative assessment of the progress of projects and challenges by visualization. In addition, it is possible to identify the positives and negatives of the planning and execution process for making decisions where improvement is required. Fuzzy concepts are determined in this paper clearly and this FIS will be helpful for proper Management solutions.

[1] AL-Janobi, A.A., 1998. Color line scan system for grading date fruits. ASAE Annual International Meeting, Orlando, Florida, USA, 12– 16 July, ASAE Paper No. 983028.
[2] H. Naderpour and S.A. Alavi Faculty of Civil Engineering Semnan University, Iran (2015), Application of Fuzzy Logic in Reinforced Concrete Structures, Civil-Comp Press, 2015 Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering.
[3] Jang, J.-S. R. and C.-T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.
[4] Mahalakshmi P and Ganesan K (2015), Mamdani fuzzy rule based model to classify sites for aquaculture development, Indian J. Fish., 62 (1): 110-115, 2015.
[5] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13, 1975.
[6] Mansour Alali, Ahmad Almogren, Mohammad Mehedi Hassan, Iehab A.L.Rassan, Md Zakirul AlamBhuiyan, Improving risk assessment model of cyber security using fuzzy logic inference system, Computers & Security, Available online 28 September 2017, https://doi.org/10.1016/j.cose.2017.09.011.
[7] Mohd Fuad Abdul Latip, Mohd Khairul Anuar Mat Udin, Muhammad Murtadha Othman, Ihsan Mohd Yassin et al., (2017), Implementation of fuzzy logic based final year project student supervisor matching system, International Journal of Advanced and Applied Sciences, 4(4) 2017, Pages: 159.
[8] N.Alavi (2012), Quality determination of Mozafati dates using Mamdani fuzzy inference system, https://doi.org/10.1016/j.jssas.2012.10.001.
[9] Salini P.S., Kedia A, Dhulipala S, KrishnaSaw, Katti B.K (2017), Spatial distribution of urban trips in recently expanded Surat city through Fuzzy Logic with various clustering Techniques: A case study of typical metropolitan city in India, https://doi.org/10.1016/j.trpro.2017.05.245.
[10] Schmilovitch, Z., Hoffman, A., Egozi, H., Grinshpun, J., Korotin, B., 2003. System determination of single date water content by novel RF device. In: Presentation at the 2003 ASAE Annual International Conference, 27–30 July 2003, Las Vegas, Nevada, USA.
[11] Schmilovitch, Z., Zaltzman, A., Hoffman, A., Edan, Y., 1995. Firmness sensor and system for date sorting. Applied Engineering in Agriculture 4, 554–560.
[12] Singh G, Kamal N. Machine vision system for tea quality determination-Tea Quality Index (TQI). IOSR Journal of Engineering. 2013; 3:46–50.
[13] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science Pub. Co., 1985.
[14] Surindra Suthar Rashmi Verma Shikhar DeepKapilKumar (2015), Optimization of conditions (pH and temperature) for Lemna gibba production using fuzzy model coupled with Mamdani’s method, https://doi.org/10.1016/j.ecoleng.2015.07.006.
[15] Tagarakis A, Koundouras S, et al. A fuzzy inference system to model grape quality in vineyards. Precis Agr. 2014; 1–24.
[16] Taner DanismanIoan Marius Bilasco Jean Martinet (2015), Boosting gender recognition performance with a fuzzy inference system, Expert Systems with Applications, Volume 42, Issue 5, 1 April 2015, Pages 2772-2784. https://doi.org/10.1016/j.eswa.2014.11.023.
[17] Tripti Rani Borah; Kandarpa Kumar Sarma, Pran Hari Talukdar(2015), Retina recognition system using adaptive neuro fuzzy inference system, 2015 International Conference on Computer, Communication and Control (IC4).
[18] Wulfsohn, D., Sarig, Y., Algazi, R.V., 1993. Defect sorting of dry dates by image analysis. Canadian Agricultural Engineering 35 (2), 133–139. Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8, 338–353. Zaid, A., 2002. Date palm cultivation, FAO publication No. 156, Rome.
[19] Zadeh, L.A., "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, pp. 28-44, Jan. 1973.
[20] Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8, 338–353. Zaid, A., 2002. Date palm cultivation, FAO publication No. 156, Rome.

Fuzzy Model, Fuzzy Inference System, Fuzzy Rules, Application.