참고문헌 (34)

1. Analysis, Database and Optimisation. Advances in Engineering Software Multidisciplinary aircraft design and evaluation software integrating CAD 37 312 2005 10.1016/j.advengsoft.2005.07.006 Hwang 

   상세보기

2. Fuselage and wing weight of transport aircraft, SAE transactions 105 1536 1996 Ardema 

3. 10.1007/3-540-45720-8_30 Abraham, A. Neuro Fuzzy Systems: State-of-the-art Modeling Techniques, 2001. 

   상세보기

4. Nauck, D. Neuro-fuzzy systems: review and prospects. 5. European Congress on Intelligent Techniques and Soft Computing (EUFIT’97). 

5. The Derivation and Application of Analytical-Statistical Weight Prediction Techniques 1969 St. John 

6. Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence 614 1997 Jang 

7. Structural Weight Estimation by the Weight Penalty Concept for Preliminary Design 1956 Hammitt 

8. Fuzzy sets* information and control 8 338 1965 10.1016/S0019-9958(65)90241-X Zadeh 

   상세보기

9. J Intelligent and Fuzzy Systems Selecting input variables for fuzzy models 4 243 1996 10.3233/IFS-1996-4401 Chiu 

   상세보기

10. Weight-Strength Analysis of Aircraft Structures 1960 Shanley 

11. J Intell Robotics Syst A Neural Network System for Patch Load Prediction 31 185 2001 10.1023/A:1012027726962 Fonseca 

    

12. J Aircr Wing mass formula for twin fuselage aircraft 29 907 1992 10.2514/3.46261 Udin 

    상세보기

13. Soft Computing Engineering Design and Manufacturing 1998 10.1007/978-1-4471-0427-8 Chawdry 

    

14. Fuzzy Set Syst Neuro-fuzzy systems for function approximation 101 261 1999 10.1016/S0165-0114(98)00169-9 Nauck 

    상세보기

15. Lotfi, A. The Importance of Learning in Fuzzy Systems. 2nd International Conference in Fuzzy Logic and Technology (EUSFLAT 2001), 2001, pp 439-442. 

16. Int J Man-Machine Studies An experiment in linguistic synthesis with a fuzzy logic controller 7 1 1975 10.1016/S0020-7373(75)80002-2 Mamdani 

    상세보기

17. Developing Highly Accurate Empirical Weight Estimating Relationships: Obstacle and Tactics 1992 Scott 

18. IEEE Transactions on ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics 23 665 1993 Jang 

19. 10.2514/6.2008-5877 Hannon, C., et al, An Alternative View on Weight Estimation for the Aircraft Industry: Problems and MDO Solutions. 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008. 

    

20. Int J Infrared and Millimeter Waves Adaptive neuro-fuzzy inference system for computing the physical dimensions of electrically thin and thick rectangular microstrip antennas 27 219 2006 10.1007/s10762-006-9070-2 Guney 

    상세보기

21. The Initial Weight Estimate 1996 Scott 

22. Mack, R.J. A Rapid Empirical Method for Estimating the Gross Takeoff Weight of a High Speed Civil Transport, 1999, NASA, TM-1999-209535. 

23. Finite Element Model Weight Estimation 1992 Droegkamp 

24. 10.1109/FUZZY.2009.5277194 Tung, W.L. and Quek, C. A mamdani-takagi-sugeno based linguistic neural-fuzzy inference system for improved interpretability-accuracy representation. in Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on. 2009. 

    

25. Design of transparent mamdani fuzzy inference systems, in Design and application of hybrid intelligent systems 468 2003 Castellano 

26. IMPACT: Innovative Mass Properties Analysis CATIA Tool 2001 Flamand 

27. Prog Phys Geog Hydrological modelling using artificial neural network 25 80 2001 10.1177/030913330102500104 Dawson 

    상세보기

28. Aircraft Weight Engineering 1996 Bechdolt 

29. On the Use of Aicraft Density in Preliminary Design 1969 Caldell 

30. IEEE Trans. Comput Fuzzy systems as universal approximators 43 1329 1994 10.1109/12.324566 Kosko 

    상세보기

31. Semi-Analytical Method for Predicting Wing Structural Mass 1995 Macci 

32. Int J Approximate Reasoning Looking for a good fuzzy system interpretability index: An experimental approach 51 115 2009 10.1016/j.ijar.2009.09.004 Alonso 

    상세보기

33. 10.1109/WAC.2006.376033 Jassbi, J.J. , et al. A Comparison of Mandani and Sugeno Inference Systems for a Space Fault Detection Application. in Automation Congress, 2006. WAC ’06. World. 2006. 

    

34. J Aircr Principal component regression for fitting wing weight data of subsonic transport 43 1925 2006 10.2514/1.21934 Rocha 

    상세보기