Fuzzy Inference System: Short Review and Design
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Fuzzy control provides a formal methodology for representing, manipulating, and implementing a human’s heuristic knowledge about how to control a system. The fuzzy control can be applied in cases where the control processes are too complex to analyze by conventional quantitative techniques or the available sources of information are interpreted qualitatively, inexactly, or uncertainly. Fuzzy controller offers many advantages to compare with other approaches to deal with uncertainty. Furthermore, fuzzy logic is well suited for low-cost implementations based on low-priced sensors, small resolution convertors of analog to digital and 4-8bit microcontroller. Such systems can be effortlessly improved by adding new rules to enhance performance or add new features. In several cases, fuzzy control can be used to advance existing conventional control systems by appending an additional layer of intelligence to the existing control method. In this research, an extensive insight of Fuzzy Inference System steps design are presented, as well as, the emphases on the fusion of fuzzy control concepts with nowadays wireless sensor networks. Review of fuzzy superior features contrary to traditional and intelligent controlling paradigms is obviously stated. Finally, the adoption of Fuzzy Inference System (FIS) approach to control problems with wireless sensor actor network is due to a number of reasons. Its algorithm needs a small size of memory, less time of execution; indeed it necessitates less mathematics than other controlling approaches.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
K. Krishnakumar, “Intelligent systems for aerospace engineering – an overview,” NASA Technical Report, Document ID: 20030105746, 2003.
.A. Byrd and R.D. Hauser, “Expert systems in production and operations management: research directions in assessing overall impact,” Int. J. Prod. Res., Vol. 29, pp. 2471-2482, 1991.
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, New Jersey, 1995.
L.A. Zadeh, “Fuzzy Logic and Soft Computing: Issues, Contentions and Perspectives,” In: Proc. of IIZUKA’94: Third Int.Conf. on Fuzzy Logic, Neural Nets and Soft Computing, Iizuka, Japan, pp. 1-2, 1994.
W. Duch, “What is Computational Intelligence and where is it going?”, In: W. Duch and J. Mandziuk, Eds., Challenges for Computational Intelligence, Springer Studies in Computational Intelligence, Vol. 63, pp. 1-13, 2007.
Zadeh, L. A. et al. 1996 Fuzzy Sets, Fuzzy Logic, Fuzzy Systems, World Scientific Press.
Imre J. Rudas, János Fodor, “Intelligent Systems” Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 , Vol. III (2008), Suppl. issue: Proceedings of ICCCC 2008, pp. 132-138
Ying, h, 2000. Fuzzy Control and Modeling analytical Foundations and Applications. IEEE Press.
R.C. Eberhart and Y. Shui, Computational Intelligence - Concepts to Implementations, Elsevier, 2007.
Imannejad, P., Karimpour, A., A new fuzzy approach on planning and decision making in engineering and construction industry, (2011) International Review on Modelling and Simulations (IREMOS), 4 (4), pp. 1974-1981.
Engelbrecht, P. A. : Computational Intelligence: An Introduction. 2007, John Wily& Sons.
Astrom KJ, MacAvoy TJ (1992). Intelligent Control: an Overview and Evaluation. In D. White and D. Sofge, Handbook on Intelligent Control, Van Nostrand Reinhold, NY. 3-34.
Y. Zhang and A. Kandel. Compensatory Genetic Fuzzy Neural Networks and Their Applications. World Scientific, 1998.
Bouslama-Bouabdallah, S., Tagina, M., A fault detection and isolation fuzzy system optimized by genetic algorithms and simulated annealing, (2010) International Review on Modelling and Simulations (IREMOS), 3 (2), pp. 212-219.
Ferreira PM, Faria EA, Ruano AE (2002). Neural network models in greenhouse air temperature prediction. Neuro computing, 43:51-75.
Hayati, M., Jamshidi, S.M., Rezaei, A., Modeling and simulation of centrifugal gas compressor using adaptive neuro-fuzzy inference system: Application to the modeling and simulation of the industrial packages, (2011) International Review on Modelling and Simulations (IREMOS), 4 (1), pp. 358-363.
Zadeh, L. (1996). Fuzzy logic computing with words. IEEE Transactions on Fuzzy Systems, 4, 103–111.
L.A. Zadeh. Soft Computing and Fuzzy Logic. IEEE Software, 11(6):48–56,1994.
T. Takagi, M, Sugeno, “ Fuzzy Identification of System and Its Applications to modeling and Control”, IEEE Transaction on Systems, Man and Cybernetics, 15, 116-132, 1985.
E.H. Mamdani and S. Assilian. An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies, 7:1–13, 1975.
Astrom KJ, MacAvoy TJ (1992). Intelligent Control: an Overview and Evaluation. In D. White and D. Sofge, Handbook on Intelligent Control, Van Nostrand Reinhold, NY. 3-34
Yager RR, Zadeh LA (1992). An introduction to fuzzy logic applications in intelligent systems. 1st Edn., Springer, NewYork.
J. Jantzen. Design of Fuzzy Controllers. Technical Report 98-E864, Department of Automation, Technical University of Denmark, 1998.
Piasecki, J.S., Zohdy, M.A., Robust hybrid complex motion control using fuzzy logic, inverse dynamic and PID-Q controllers, (2013) International Review of Automatic Control (IREACO), 6 (1), pp. 19-28.
Salim, M.S., Abd Malek, M.F., Sabri, N., Noaman, N.M., Juni, K.M., Abu Talib, N.A.B., A new ultrasonic exponential decay pulser technique for low concentrations detection and measurements, (2013) International Review of Automatic Control, (IREACO), 6 (2), pp. 155-167.
Varanon Uraikul, Christine W. Chan, Paitoon Tontiwachwuthikul, 2007.Artificial intelligence for monitoring and supervisory control of process systems. Engineering Applications of Artificial Intelligence 20 (2007) 115–131.
Naseer Sabri, S.A. Aljunid, R.B. Ahmad, Abid Yahya, R. Kamaruddin and M.S. Salim, 2011. ―Wireless Sensor Actor Network Based on Fuzzy Inference System for Greenhouse Climate Control. Journal of Applied Sciences, 11: 3104-3116
Subbaramaiah, K., Veera Reddy, V.C., Design of fuzzy logic controller for automatic generation control of TCPS based hydrothermal system under deregulated scenario, (2011) International Review on Modelling and Simulations (IREMOS), 4 (3), pp. 1248-1256.
Collotta M, Pau G, Salerno VM, Scata G (2011). A fuzzy based algorithm to manage power consumption in industrial Wireless Sensor Networks. 9th IEEE International Conference on Industrial Informatics, INDIN 2011.
Barrios JA, Torres-Alvarado M, Cavazos A (2012). Neural, fuzzy and Grey-Box modeling for entry temperature prediction in a hot strip mill. Expert Systems with Applications. 39(3):3374-3384.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2020 Praise Worthy Prize