Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification

Neural Processing Letters

Volumn 27, Issue 3, 2008, Pages 285-296

  Subudhi, Bidyadhar ^a   Jena, Debashisha ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Differential evolution; Evolutionary computation; Levenberg Marquardt; Nonlinear system identification

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; DIFFERENTIAL EQUATIONS; EVOLUTIONARY ALGORITHMS; NONLINEAR SYSTEMS; OPTIMIZATION;

DIFFERENTIAL EVOLUTION (DE); LEVENBERG MARQUARDT ALGORITHM (LM); NONLINEAR SYSTEM IDENTIFICATION;

NEURAL NETWORKS;

EID: 42449127855     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-008-9077-x     Document Type: Article

References (7)

1. Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50: 1873-1896
2. Billings SA, Wei HL (2005) A new class of wavelet networks for nonlinear system identification. IEEE Trans Neural Networks 16: 862-874
3. Narendra KS, Parthaasarathy K (2005) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Networks 16: 8624-8874
4. Storn R (1999) System design by constraint adaptation and differential evolution. IEEE Trans Evol Comput 3: 22-34
5. Ilonen J, Kamarainen JK, Lampinen J (2003) Differential evolution training algorithm for feed forward neural networks. Neural Proc Lett 17: 93-105
6. Lin C-T, George Lee CS (1996) Neural Fuzzy Systems: a Neuro-fuzzy synergism to intelligent systems. PHI, Inc., New Jersey
7. Box GEP, Jenkins GM (1970) Time series analysis, forecasting and control. Holden Day, San Francisco