Genetic algorithm with an improved fitness function for (N)ARX modelling

Mechanical Systems and Signal Processing

Volumn 21, Issue 2, 2007, Pages 994-1007

  Chen, Q ^a   Worden, K ^b   Peng, P ^a   Leung, A Y T ^c  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

ARX model; Fitness function; Genetic algorithm; Model structure

Indexed keywords

COMPUTER SIMULATION; CORRELATION METHODS; FITS AND TOLERANCES; MODEL STRUCTURES; PREDICTIVE CONTROL SYSTEMS; SIGNAL PROCESSING;

AUTO-REGRESSIVE WITH EXOGENOUS INPUTS (ARX); DYNAMIC SYSTEMS; EVOLUTION PROCESSES; FITNESS FUNCTIONS;

GENETIC ALGORITHMS;

EID: 33750497791     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2006.01.011     Document Type: Article

References (13)

1. K. Worden, G.R. Tomlinson, Nonlinearity in Structural Dynamics, IOP Publishing Ltd., Amsterdam, 2001.
2. Chen Q., and Tomlinson G.R. Parametric identification of systems with dry friction and nonlinear stiffness using a time series model. Journal of Vibration and Acoustics 118 (1996) 252-263
3. Masri S.F., Chassiakos A.G., and Caughey T.K. Structure-unknown non-linear dynamic systems: identification through neural networks. Smart Materials and Structures 1 (1992) 45-56
4. Goodwin G.C., Gevers M., and Ninness B. Quantifying the error in estimated transfer functions with applications to model order selection. IEEE Transactions on Automatic Control 37 (1992) 913-929
5. L. Ljung, L. Guo, The role of model validation for assessing the size of the unmodelled dynamics, IEEE Transactions on Automatic Control 42(9) (1997).
6. Sarimveis H., Alexandridis A., Mazarakis S., and Bafas G. A new algorithm for developing dynamic radial basis function neural network model based on genetic algorithms. Computers and Chemical Engineering 28 (2004) 209-217
7. Chen S., and Billings S.A. Representations of nonlinear systems: the NARX model. International Journal of Control 49 (1989) 1013-1032
8. Chen Q., and Tomlinson G.R. A new type of time series model for the identification of nonlinear system. Mechanical Systems and Signal Processing 8 5 (1994) 531-549
9. K. Rodriguez-Vazquez, C.M. Fonseca, P.J. Fleming, Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 34(4) (2004).
10. Rawlins G.J.E. Foundations of Genetic Algorithms (1991), Morgan Kaufmann Publishers, San Meteo, CA
11. J. Baker, J. Grefenstette, How genetic algorithms work: a critical look at implicit parallelism, in: Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, Palo Alto, CA, 1989, pp. 20-27.
12. Goldberg D.E. Genetic Algorithms in Search, Optimisation and Machine Learning (1989), Addison-Wesley, Reading, MA
13. Billings S.A., and Voon W.S.F. Correlation based validity tests for non-linear models. International Journal of Control 44 1 (1986) 235-244