References
- B. Hao, Definition of complexity and complexity science, Sci. 51(1999) 3–5.
- W. Yao, M. Chen, J. Matas, J.M. Guerrero, Z.M. Qian, Design and analysis of the droop control method for parallel inverters considering the impact of the complex impedance on the power sharing, IEEE Trans. Ind. Electron. 58(2011) 576–588. doi: 10.1109/TIE.2010.2046001
- F. Ding, System Identification Theory and Methods, China Electric Power Press, Beijing, 2012.
- L. Ljung, System Identification: Theory for the User, 2nd ed., Prentice Hall, Englewood Cliffs, 1999.
- G.B. Giannakis, E. Serpedin, A bibliography on nonlinear system identification, Signal Process. 81(2001) 533–580. doi: 10.1016/S0165-1684(00)00231-0
- A. Juditsky, H. Hjalmarsson, A. Benveniste, B. Deylon, L. Ljung, J. Sjöberg, Q.H. Zhang, Nonlinear black-box models in system identification: mathematical foundations, Automatica 31(1995) 1725–1750. doi: 10.1016/0005-1098(95)00119-1
- S.A. Liu, F. Tang, Study on system identification based on genetic algorithm method, Syst. Eng. Theo. Pract. 3(2007) 134–137.
- X. Hong, R.J. Mitchell, S. Chen, C. Harris, K. Li, G.W. Irwin, Model selection approaches for nonlinear system identification: a review, Int. J. Syst. Sci. 39(2008) 925–946. doi: 10.1080/00207720802083018
- H. Hjalmarsson, System identification of complex and structured systems, Eur. J. Contr. 14(2009) 275–310. doi: 10.3166/ejc.15.275-310
- Y.G. Tang, L.J. Qiao, X.P. Guan, Identification of Wiener model using step signals and particle swarm optimization, Expert Syst. Appl. 37(2010) 3398–3404. doi: 10.1016/j.eswa.2009.10.008
- M. Niedźwiecki, S. Gackowski, New approach to noncausal identification of nonstationary stochastic FIR systems subject to both smooth and abrupt parameter changes, IEEE Trans. Autom. Control 58(2013) 1847–1853. doi: 10.1109/TAC.2013.2238995
- J.C. Spall, Identification for systems with binary subsystems, IEEE Trans. Autom. Control 59(2014) 3–17. doi: 10.1109/TAC.2013.2275664
- J. Sjöberd, J. Schoukens, Initializing Wiener-Hammerstein models based on partitioning of the best linear approximation, Automatica 48(2012) 353–359. doi: 10.1016/j.automatica.2011.07.007
- K.Y. Xing, M.C. Zhou, H.X. Liu, F. Tian, Optimal Petri-net-based polynomial-complexity deadlock-avoidance policies for automated manufacturing systems, IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, 39(2009)188–199. doi: 10.1109/TSMCA.2008.2007947
- A. Bellouquid, E. De Angelis, L. Fermo, Towards the modeling of vehicular traffic as a complex system: a kinetic theory approach, Math. Mod. Meth. Appl. Sci. 22(2012) 1–35. doi: 10.1142/S0218202511400033
- J.F. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann Pub, San Francisco, 2001.
- E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: from Natural to Artificial Systems, Oxford University Press, Washington, 1999.
- X.L. Li, Z.J. Shao, J.X. Qian, An optimizing method based on autonomous animals: fish-swarm algorithm, Syst. Eng. Theo. Pract. 22(2002) 32–38.
- M.Y. Jiang, D.F. Yuan, Artificial Fish Swarm Algorithm and its Applications. In: Proc. of International Conference on Sensing, Computing and Automation, Chongqing, China, 5, 1782(2006).
- J.M. Xiao, X.M. Zheng, X.H. Wang, A Modified Artificial Fish-Swarm Algorithm. In: Proc. of IEEE the 6th World Congress on Intelligent Control and Automation, Dalian, China, 10, 3456(2006).
- X.J. Shan, M.Y. Jiang, J.P. Li, The Routing Optimization Based on Improved Artificial Fish Swarm Algorithm. In: Proc. of IEEE the 6th World Congress on Intelligent Control and Automation, Dalian, China, 10, 3658(2006).
- C.R. Wang, C.L. Zhou, J.W. Ma, An Improved Artificial Fish-Swarm Algorithm and Its Application in Feed-Forward Neural Networks. In: Proc. of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, China, 12, 2890(2005).
- Y. Yu, Y.F. Tian, Z.F. Yin, Multiuser Detector Based on Adaptive Artificial Fish School Algorithm. In: Proc. of the IEEE International Symposium on Communication and Information Technology, Beijing, China, 5, 1480(2005).
- M.Y. Jiang, D.F. Yuan, Wavelet Threshold Optimization with Artificial Fish Swarm Algorithm. In: Proc. of the IEEE International Conference on Neural Networks and Brain, Beijing, China, 6, 569(2005).
- M.F. Zhang, S. Cheng, F.C. Li, Evolving Neural Network Classifiers and Feature Subset Using Artificial Fish Swarm. In: Proc. of the IEEE International Conference on Mechatronics and Automation, Luoyang, China, 12, 1598(2006).
- M.Y. Jiang, Y. Wang, F. Rubio, Spread Spectrum Code Estimation by Artificial Fish Swarm Algorithm. In: Proc. of the IEEE Symposium on Intelligent Signal Processing, Alcalá de Henares, Spain, 9, 569(2005).
- S. Farzi, Efficient job scheduling in grid computing with modified artificial fish swarm algorithm, Int. J. Compu. Theo. Eng. 1(2009) 1793–8201.
- G.R. Zheng, Z.C. Lin, A winner determination algorithm for combinatorial auctions based on hybrid artificial fish swarm algorithm, Phys. Proced. 25 (2012) 1666–1670. doi: 10.1016/j.phpro.2012.03.292
- M. Neshat, G. Sepidnam, M. Sargolzaei, A.N. Toosi, Artificial fish swarm algorithm: a survey of the state of the art, hybridization, combinatorial and indicative applications, Artif. Intell. Rev. 34(2012) 60–93.