Nonlinear system identification using optimized dynamic neural network

Neurocomputing

Volumn 72, Issue 13-15, 2009, Pages 3277-3287

  Xie, W F ^a   Zhu, Y Q ^a   Zhao, Z Y ^a   Wong, Y K ^b  

^a Dept of Electrical and Computer Engineering   (Canada)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Artificial neural network; Genetic algorithm; Nonlinear system identification; On line adaptation

Indexed keywords

DEEP NEURAL NETWORKS; ENCODING (SYMBOLS); GENETIC ALGORITHMS; MATRIX ALGEBRA; MEMORY ARCHITECTURE; NETWORK ARCHITECTURE; NEURAL NETWORKS; NONLINEAR SYSTEMS; RELIGIOUS BUILDINGS; TIME DELAY;

ARCHITECTURE OPTIMIZATION; CONNECTION MATRICES; DYNAMIC NEURAL NETWORKS; ENCODING SCHEMES; HYBRID TRAINING; INPUT TIME DELAYS; LINEAR PARAMETERS; ON-LINE ADAPTATION;

COMPUTER ARCHITECTURE;

ADAPTATION; ARTICLE; CONTROLLED STUDY; DYNAMIC NEURAL NETWORK; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL PARAMETERS; NERVE CELL; NONLINEAR SYSTEM; ONLINE SYSTEM; PRIORITY JOURNAL; TIME; WEIGHT;

EID: 79952763605     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.02.004     Document Type: Article

References (20)

1. V.M. Becerra, F.R. Garces, S.J. Nasuto, W. Holderbaum, An efficient parameterization of dynamic neural networks for nonlinear system identification, IEEE Transactions on Neural Networks 16 (2005) 983-988.
2. D.L. Chester, Why two hidden layers are better than one, in: International Joint Conference on Neural Networks, 1990.
3. M. Frean, The upstart algorithm: a method for constructing and training feedforward neural networks, Neural Computation 2 (1990) 198-209.
4. M.M. Gupta, L. Jin, N. Homma, Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, Wiley Interscience, New York, 2003.
5. Y. Hirose, K. Yamashita, S. Hijiya, Back-propagation algorithm which varies the number of hidden units, Neural Networks 4 (1991) 61-66.
6. J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, 1975.
7. H. Kitano, Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms, Physica D 75 (1994) 225-228.
8. J. Madár, J. Abonyi, F. Szeifert, Genetic programming for the identification of nonlinear input-output models, Industrial and Engineering Chemistry Research 44 (2005) 3178-3186.
9. M. Melanie, An Introduction to Genetic Algorithms, The MIT Press, Cambridge, MA, London, England, 1999.
10. G.F. Miller, P.M. Todd, S.U. Hegde, Designing neural networks using genetic algorithms, in: The Third International Conference on Genetic Algorithms and Their Applications, 1989, pp. 379-384.
11. K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks 1 (1990) 4-27.
12. V. Prasad, B.W. Bequette, Nonlinear system identification and model reduction using artificial neural networks, Computers and Chemical Engineering 27 (2003) 1741-1754.
13. U. Ramacher, Hamiltonian dynamics of neural networks, Neural Networks 6 (1993) 547-557.
14. A. Roy, L.S. Kim, S. Mukhopadhyay, A polynomial time algorithm for the construction and training of a class of multilayer perceptrons, Neural Networks 6 (1993) 535-545.
15. A. Weigend, On overfitting and the effective number of hidden units, in: Presented at Connectionist Models Summer School, 1994, pp. 335-342.
16. X. Yao, Evolving artificial neural networks, in: Proceedings of the IEEE, vol. 87, no. 7, 1999, pp. 1423-1447.
17. A. Yazdizadeh, K. Khorasani, Adaptive time delay neural network structures for nonlinear system identification, Neurocomputing 47 (2002) 207-240.
18. W. Yu, Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms, Information Sciences 158 (2004) 131-147.
19. K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies, Evolutionary Computation 10 (2) (2002) 99-127.
20. H. Tsukimoto, H. Hatano, The functional localization of neural networks using genetic algorithms, Neural Networks 56 (16) (2003) 55-67.