1[1]Takahisa Kobayashi,Donald L.Simon,A Hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics,AIAA-2001-3763,37th AIAA/SAE/ASME/ASEE Joint Propulsion Conference and Exhibit,8-11 July 2001,Salt Lake City,Utah.
2[2]Takahisa Kobayashi,Donald L.Simon,Application of a bank of kalman filters for aircraft engine fault diagnostics,NASA/TM-2003-212526.
3[3]K KrishnaKumar,Y Hachisako,Y Huang.Jet Engine Performance Estimation Using Intelligent System Technologies,AIAA-2001-1122,39th AIAA Aerospace Sciences Meeting & Exhibit,8 ~ 11 January 2001,Reno,NV.
4[4]Randall Bickford,Donald Malloy.Development of a Real -Time Turbine Engine Diagnostic System,AIAA-2002 -4306,38th AIAA/ SAE/ ASME/ASEE Joint Propulsion Conference and Exhibit,7 ~ 10 July 2002,Indianapolis,Indiana.
5[5]Santanu Chatterjee,Jonathan S.Litt,Online Model Parameter Estimation of Jet Engine Degradation for Autonomous Propulsion Control,AIAA-2003-5425,AIAA Guidance,Navigation,and Control Conference and Exhibit,11 ~ 14August 2003,Austin,Texas.
6[6]Allan J Volpeni,Tom Brotherton,Robert Luppold,Development of an Information Fusion System for Engine Diagnostics and Health Management,AIAA 2004 -6461,AIAA 1st Intelligent System Technical Conference,20 ~ 22 September 2004,Chicago,Illinois.
8[9]I S Diakunchak,Performance Deterioration in Industrial Gas Turbines,Journal of Engineering for Gas Turbines and Power,1992,114(2):161~168.
9[10]H Luppold,J R Roman,G.W.Gallops,at all.Estimating in -flight performance variations using kalman filter concepts,AIAA-89-2584,AIAA/ SAE/ASME/ASEE 25th Joint Propulsion Conference,July 10 ~ 121989,Monterey,CA.
10[11]Klaus Lietzau,Andreas Kreiner.Model based control concepts for jet engines,Proceedings of ASME TURBO EXPO 2001,June 4 ~ 7,2001,New Orleans,Louisiana,USA.
二级参考文献12
1Wolpert D H, Macready W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997,(1): 67-82.
2Zbigniew Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs[M]. Berlin Heidelberg: Springer-Verlag, 1996.
3Jones T, Forrest S. Fitness distance correlation as a measure of problem difficulty for genetic algorithms[A]. In: Proceedings of the 6 th International Conference on Genetic Algorithms[C]. San Mateo, CA: Morgan Kaufmann, 1995. 184-192.
4Jones T. Evolutionary Algorithms, Fitness Landscapes and Search[D]. Albuquerque: University of New Mexico, 1995.
5Weinberger E D. Correlated and uncorrelated fitness landscapes and how to tell the difference[J]. Biological Cybernetics, 1990,63: 325-336.
6Merz P, Freisleben B. Fitness landscape analysis and memetic algorithms for the quadratic assignment problem[J]. IEEE Transactions on Evolutionary Computation, 2000, (4): 337-352.
7Naudts B, Kallel L. A comparison of predictive measures of problem difficulty in evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2000, (1): 1-15.
8Davidor Y. Epistasis variance: A viewpoint on GA-hardness[A]. In: Rawlins G J E ed. Foundations of Genetic Algorithms[M].San Mateo, CA: Morgan Kaufmann, 1991. 23-35.
9复旦大学.概率论(第2册 数理统计)[M].北京:高等教育出版社,1995.117-122.
10Kramer M A. Autoassociative neural networks[J]. Computers Chem Engng,1992,116(4): 313-328.
3Holland J H.Adaptation in natural and artificial systems[M].Ann Arbor:University of Michigan Press,1975:20-30.
4Kennedy J,Eberhart R.Particle swarm optimization[C]//In:IEEE Int'l Conf On Neural Networks,Perth,Australia,1995:1942-1948.
5Eberhart R,Kennedy J.A new optimization using particle swarm theory[C]//In:Proc.of the Sixth International Symposium on Micro Machine and Human Science,Nagoya,Japan,1995:39-43.