This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Nandi S, Toliyat H.A, LI X. Condition monitoring and fault diagnosis of electrical motors–a review [J]. IEEE Trans. on Energy Conversion, 2005, 20(4): 719-72910.1109/TEC.2005.847955Search in Google Scholar
Diallo D, Benbouzid M.E.H, Makouf A, A fault tolerant control architecture for induction motor drives in automotive applications [J]. IEEE Trans. on Vehicular Technology, 2004, 53(6): 1847-185510.1109/TVT.2004.833610Search in Google Scholar
Denker, J.S, Neural Network Models of Learning and Adaptation, Physica 22D, 1986.10.1016/0167-2789(86)90242-3Search in Google Scholar
Lu, H., Setiono, R, and Liu, H, “Effective Data Mining Using Neural Networks”, IEEE Trans. On Knowledge and Data Engineering, 1996, 8(6), pp. 957-961.10.1109/69.553163Search in Google Scholar
Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl.Acad. Sci. USA, 1982, 79(8): 2554-2558.Search in Google Scholar
Rumelhart D E, McClell J L. Parallel Distributed Processing, Vol. 1-2. Cambridge, MA, USA: MIT Press, 1986.10.7551/mitpress/5236.001.0001Search in Google Scholar
Dong Mingchui, Cheng Takson, Chan Sileong. On-line fast motor fault diagnostics based on fuzzy neural networks.Tsinghua Science and Technology, 2009, 14(2): 225-233.10.1016/S1007-0214(09)70034-3Search in Google Scholar
Psillakis H E. Further results on the use of Nussbaum gains in adaptive neural network control. IEEE Transactions on Automatic Control, 2010, 55(12): 2841-2846.10.1109/TAC.2010.2078070Search in Google Scholar
Sun Ming, Zhao Lin, Cao Wei, et al. Novel hysteretic noisy chaotic neural network for broadcast scheduling problems in packet radio networks. IEEE Transactions on Neural Networks, 2010, 21(9): 1422-1433.10.1109/TNN.2010.2059041Search in Google Scholar
Liang Yao, Liang Xu. Improving signal prediction performance of neural networks through multi resolution learning approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, 36(2):341-352.10.1109/TSMCB.2005.857092Search in Google Scholar
Guo W W, Li M, Li Z, et al. Approximating nonlinear relations between susceptibility and magnetic contents in rocks using neural networks. Tsinghua Science and Technology, 2010, 15(3): 281-287.10.1016/S1007-0214(10)70062-6Search in Google Scholar
Khomfoi S, Tolbert L M. Fault diagnostic system for a multilevel inverter using a neural network. IEEE Transactions on Power Electronics, 2007, 22(3): 1062-1069.10.1109/TPEL.2007.897128Search in Google Scholar
Gui Liang Yin and Li Ping Xiao, “Squirrel-Cage Motors Fault Diagnosis Using Immunology Principles”, Proceedings of the Chinese Society for Electrical Engineering, Beijing, 2003, 6, pp. 132-136.Search in Google Scholar
Dasgupta, D., “An Overview of Artificial Immune System and Their Applications”, Artificial Immune System and Their Applications, Spring-Verlag, 1998b, pp. 3-21.10.1007/978-3-642-59901-9_1Search in Google Scholar
Mehdi Neshat, Ali Adeli. A Review of artificial fish swarm optimization methods and applications. INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 5, NO. 1, MARCH 201210.21307/ijssis-2017-474Search in Google Scholar
Farmer J D, Packard N H, Perelson A A. The immune system adaptation and machine learning. Physica, 1986, 22D:187-204.10.1016/0167-2789(86)90240-XSearch in Google Scholar
Castro P A D, Von Zuben F J. Learning ensembles of neural networks by means of a Bayesian artificial immune system. IEEE Transactions on Neural Networks, 2011, 22(2): 304316.Search in Google Scholar
Yuan H C, Xiong F L, Huai X Y. A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy. Computers and Electronics in Agriculture, 2003, 40(1-3): 57-64.10.1016/S0168-1699(03)00011-5Search in Google Scholar
A. F. Salami, H. Bello-Salau. A novel biased energy distribution (BED) technique for cluster-based routing in wireless sensor networks [J]. International journal on smart sensing and intelligent systems vol. 4, NO. 2, June 201110.21307/ijssis-2017-433Search in Google Scholar
Chun J S, Lim J P, Jung H K. Optimal design of synchronous motor with parameter correction using immune algorithm. IEEE Trans. on Energy Conversion, 1999, 14(3):610-615.Search in Google Scholar
De Castro L N, von Zuben F J. The clonal selection algorithm with engineering applications. In: Whitley L D, Goldberg D E, et al, eds. Proc. of the GECCO 2000. San Fransisco: Morgan Kaufman Publishers, 2000. 36-37Search in Google Scholar
GONG Mao-Guo, HAO Lin. Data reduction based on artificial immune system [J]. Journal of Software, Vol.20, No.4, April 2009, pp.804-814 (in Chinese)Search in Google Scholar
Surya S,Mack G W,Powers E J et al. Characterization of distribution power quality events with flourier and wavelet transforms [J].IEEE Transactions on Power Delivery,2000,15 (1):247-25410.1109/61.847259Search in Google Scholar
M. G. Gong, H. F. Du, and L. C. Jiao, “Optimal approximation of linear systems by artificial immune response,” Sci. China Series F: Inf. Sci., vol. 49, no. 1, pp. 63–79, Jan. 2006.10.1007/s11432-005-0314-xSearch in Google Scholar
Jiao Licheng, Li Yangyang, Gong Maoguo, et al. Quantum-inspired immune clonal algorithm for global optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38(5): 1234-1253.10.1109/TSMCB.2008.92727118784009Search in Google Scholar
Thomson, W. T., & Fenger, M. Current signature analysis to detect induction motor faults. IEEE Industrial Applications Magazine, 2001, 7, 26-34.10.1109/2943.930988Search in Google Scholar
Aydin, I., et al. A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Applied Soft Computing Journal. doi:10.1016/j.asoc.2009.11.003.10.1016/j.asoc.2009.11.003Search in Google Scholar