International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 10, No. 4, 8 Apr. 2018
Cover page and Table of Contents: PDF (size: 889KB)
Fuzzy Rule Based Inference System for Implementation of Naval Military Mission
Full Text (PDF, 889KB), PP.28-37
Views: 0 Downloads: 0
Author(s)
Rashmi Singh
1,*
Vipin Saxena
1
Index Terms
Inference system, fuzzy concept, analytical fact, naval military, hardware
Abstract
Naval military units are convoluted frameworks required to work in specific time periods in seaward assignments where support operations are radically restricted. A decline at the time of mission is an analytical fact that can radically impact the mission achievement. The choice of changing a unit to a mission subsequently requires complex judgments including data about the well being status of hardware and the natural conditions. The present system expects to help the choice about changing a unit to a mission considering that ambiguity and unpredictability of information by methods of fuzzy concepts and imitates the selection procedure of a human trained by means of a rule-based inference system. A numerical application is introduced to demonstrate the viability of the approach.
Cite This Paper
Rashmi Singh, Vipin Saxena, "Fuzzy Rule Based Inference System for Implementation of Naval Military Mission", International Journal of Computer Network and Information Security(IJCNIS), Vol.10, No.4, pp.28-37, 2018. DOI:10.5815/ijcnis.2018.04.04
Reference
[1]Singh K., “Fuzzy Logic Based Modified Adaptive Modulation Implementation for Performance Enhancement in OFDM Systems”, International Journal of Intelligent Systems and Applications, vol. 8(5), pp.49-54, 2016.
[2]Mustapha S. et al., “Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators”, International Journal of Intelligent Systems and Applications, vol. 11, pp.49-56, 2014.
[3]Olugu E.U., Wong K.Y., “An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry”, Expert Systems with Applications, vol. 39(1), pp. 375-384, 2012.
[4]Akgun A. et al., “An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm”, Computers & Geosciences, vol. 38(1), pp. 23-34, 2012.
[5]Amindoust A. et al., “Sustainable supplier selection: A ranking model based on fuzzy inference system”, Applied Soft Computing, vol. 12(6), pp. 1668-1677, 2012.
[6]Singh M., Chandra A., “Application of adaptive network-based fuzzy inference system for sensorless control of PMSG-based wind turbine with nonlinear-load-compensation capabilities”, IEEE transactions on power electronics, vol. 26(1), pp. 165-175, 2011.
[7]Büyük?zkan G., ?if?i G., “A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information”, Computers in Industry, vol. 62(2), pp. 164-174, 2011.
[8]Fernández A. et al., “Solving multi-class problems with linguistic fuzzy rule based classification systems based on pair wise learning and preference relations”, Fuzzy sets and systems, vol. 161(23), pp. 3064-3080, 2010.
[9]Ustundag A. et al., “Fuzzy rule-based system for the economic analysis of RFID investments”, Expert systems with applications, vol. 37(7), pp. 5300-5306, 2010.
[10]Kurnaz S. et al., “Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles”, Expert Systems with Applications, vol. 37(2), pp. 1229-1234, 2010.
[11]Boyacioglu M.A., Avci D., “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock
exchange”, Expert Systems with Applications, vol. 37(12), pp. 7908-7912, 2010.
[12]Aarabi A. et al., “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG”, Clinical Neurophysiology, vol. 120(9), pp. 1648-1657, 2009.
[13]Zarandi M.F. et al., “A type-2 fuzzy rule-based expert system model for stock price analysis”, Expert Systems with Applications, vol. 36(1), pp. 139-154, 2009.
[14]Fernández A. et al., “Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets”, International Journal of Approximate Reasoning, vol. 50(3), pp.561-577, 2009.
[15]Fernández A. et al., “On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets”, Expert Systems with Applications, vol. 36(6), pp. 9805-9812, 2009.
[16]Quek C. et al., “A novel self-organizing fuzzy rule-based system for modelling traffic flow behavior”, Expert Systems with applications, vol. 36(10), pp. 12167-12178, 2009.
[17]Chang P.C., Liu C.H., “A TSK type fuzzy rule based system for stock price prediction”, Expert Systems with applications, vol.34 (1), pp. 135-144, 2008.
[18]Jahromi M.Z., Taheri M., “A proposed method for learning rule weights in fuzzy rule-based classification systems”, Fuzzy Sets and Systems, vol. 159(4), pp. 449-459, 2008.
[19]Fernández A. et al., “A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets”, Fuzzy Sets and Systems, vol. 159(18), pp. 2378-2398, 2008.
[20]Ying L.C., Pan M.C., “Using adaptive network based fuzzy inference system to forecast regional electricity loads”, Energy Conversion and Management, vol. 49(2), pp. 205-211, 2008.
[21]Keshwani D.R. et al., “Rule-based Mamdani-type fuzzy modeling of skin permeability”, Applied Soft Computing, vol. 8(1), pp. 285-294, 2008.
[22]Angelov P. and Zhou X., “On line learning fuzzy rule-based system structure from data streams”, IEEE International conference on fuzzy systems (FUZZ2008), pp. 915-922, 2008.
[23]Mansoori E.G., et al., “A weighting function for improving fuzzy classification systems performance”, Fuzzy sets and systems, vol. 158(5), pp. 583-591, 2007.
[24]Sun Z.L. et al., ”A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37(5), pp. 1321-1331, 2007.
[25]Firat M., Güng?r M., “River flow estimation using adaptive neuro fuzzy inference system”, Mathematics and Computers in Simulation, vol. 75(3), pp. 87-96, 2007.
[26]Polat K., Güne? S., “An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease”, Digital Signal Processing, vol. 17(4), pp. 702-710, 2007.
[27]Chang F.J., Chang Y.T., “Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in water resources, vol. 29(1), pp. 1-10, 2006.
[28]Polat K., Güne? S., “A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system”, Digital Signal Processing, vol. 16(6), pp. 913-921, 2006.
[29]Rong H.J. et al., “Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction”, Fuzzy sets and systems, vol. 157(9), pp. 1260-1275, 2006.
[30]Kazeminezhad M.H. et al., “Application of fuzzy inference system in the prediction of wave parameters”, Ocean Engineering, vol. 32(14), pp. 1709-1725, 2005.
[31]Güler I., übeyli E.D., “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients”, Journal of neuroscience methods, vol. 148(2), pp. 113-121, 2005.
[32]Chakraborty D., Pal N.R., “A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification”, IEEE Transactions on Neural Networks, vol. 15(1), pp.110-123, 2004.
[33]Lu D., Antony J., “Optimization of multiple responses using a fuzzy-rule based inference system”, International Journal of Production Research, vol. 40(7), pp. 1613-1625, 2002.
[34]Ho S.Y. et al., “Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system”, International Journal of Machine Tools and Manufacture, vol. 42(13), pp. 1441-1446, 2002.
[35]Kasabov N.K., Song Q., “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction”, IEEE transactions on Fuzzy Systems, vol. 10(2), pp. 144-154, 2002.
[36]Cordón O. et al., “Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base”, IEEE Transactions on fuzzy systems, vol. 9(4), pp.667-674, August 2001.
[37]Ishibuchi H., Nakashima T., “Effect of rule weights in fuzzy rule-based classification systems”, IEEE Transactions on Fuzzy Systems, vol. 9(4), pp. 506-515, august 2001.
[38]Chakrborty D., Pal N.R., “Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 31(3), pp. 391-400, June 2001.
[39]Cordón O. et al., “A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base”, Information Sciences, vol. 136(1), pp. 85-107, 2001.
[40]Casillas J. et al., “Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems”, Information Sciences, vol. 136(1), pp. 135-157, 2001.
[41]Cordón O. et al., “A proposal on reasoning methods in fuzzy rule-based classification systems”, International Journal of Approximate Reasoning, vol. 20(1), pp. 21-45, 1999.
[42]Ishibuchi H. et al., “Voting in fuzzy rule-based systems for pattern classification problems”, Fuzzy sets and systems, vol. 103(2), pp. 223-238, 1999.
[43]Kuo R.J., Xue K.C., “Fuzzy neural networks with application to sales forecasting”, Fuzzy Sets and Systems, vol. 108(2), pp. 123-143, 1999.
[44]??ski J., Czoga?a E., “A new artificial neural network based fuzzy inference system with moving consequents in if–then rules and selected applications”, Fuzzy Sets and Systems, vol. 108(3), pp. 289-297, 1999.
[45]Tolias Y.A., Panas S.M., “On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system”, IEEE Signal Processing Letters, vol. 5(10), pp.2 45-247, October 1998.
[46]Bernard J.A., “Use of a rule-based system for process control”, IEEE Control Systems Magazine, vol. 8(5), pp.3-13, October 1998.
[47]Jouffe L., “Fuzzy inference system learning by reinforcement methods”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 28(3), pp. 338-355, 1998.
[48]Nozaki K. et al.,”Adaptive fuzzy rule-based classification systems”, IEEE Transactions on fuzzy Systems, vol. 4(3), pp. 238-250,1996.
[49]Sun C.T., “Rule-base structure identification in an adaptive-network-based fuzzy inference system”, IEEE Transactions on Fuzzy Systems, vol. 2(1), pp. 64-73, 1994.
[50]Jang J.S., “ANFIS: adaptive-network-based fuzzy inference system”, IEEE transactions on systems, man, and cybernetics, vol. 23(3), pp. 665-685, 1993.