Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm

Neural Computing and Applications

Volumn 21, Issue 6, 2012, Pages 1281-1295

  Loghmanian, Sayed Mohammad Reza ^a   Jamaluddin, Hishamuddin ^b   Ahmad, Robiah ^b   Yusof, Rubiyah ^a   Khalid, Marzuki ^a  

^a MALAYSIA JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY   (Malaysia)

^b UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Artificial neural network; Model structure selection; Multi objective genetic algorithm; NSGA II; System identification

Indexed keywords

ADEQUATE MODELS; ALTERNATIVE ALGORITHMS; DYNAMIC SYSTEM MODELING; FUNCTION APPROXIMATION; MODEL PARAMETERS; MODEL STRUCTURE SELECTION; MULTI OBJECTIVE OPTIMIZATIONS (MOO); MULTI-OBJECTIVE GENETIC ALGORITHM; NETWORK STRUCTURES; NEURAL NETWORK MODEL; NEURAL NETWORK TOPOLOGY; NON-LINEAR DYNAMIC SYSTEMS; NSGA-II; OPTIMUM STRUCTURES; PARETO-OPTIMAL SETS; PREDICTIVE ACCURACY; PRIOR KNOWLEDGE; PROCESS DATA; STRUCTURE OPTIMIZATION;

ALGORITHMS; COMPUTER SIMULATION; DYNAMICAL SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LINEAR CONTROL SYSTEMS; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS;

STRUCTURAL OPTIMIZATION;

EID: 84865650295     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0560-3     Document Type: Article

References (29)

1. Hagan MT, Demuth MT, Beale MH (1996) Neural network design. PWS Publishing, Boston.
2. Billings SA, Jamaluddin H, Chen S (1992) Properties of neural networks with applications to modeling non-linear dynamical systems. Int J Control 55(1): 193-224.
3. Chen S, Billings SA, Grant PM (1990) Non-linear system identification using neural networks. Int J Control 51(6): 1191-1214.
4. Xua KJ, Zhang J, Wang XF, Teng Q, Tan J (2008) Improvements of nonlinear dynamic modeling of hot-film MAF sensor. Sens Actuators A 147: 34-40.
5. Caruana R, Lawrence S, Giles CL (2001) Overfitting in neural networks: backpropagation, conjugate gradient, and early stopping. Adv Neural Inf Process Syst 13: 402-408.
6. Bebis G, Georgiopoulos M (1994) Feed-forward neural networks: why network size is so important. IEEE Potentials 13(4): 27-31.
7. Sietsma J, Dow RJF (1988) Neural net pruning-why and how. In: Proceedings of the IEEE international conference on neural networks, San Diego, pp 325-333.
8. Ahmad R, Jamaluddin H, Hussain MA (2004) Model structure selection for discrete-time nonlinear systems using genetic algorithm. J Syst Control Eng 218(12): 85-98.
9. SK Oh, Pedrycz W (2006) Genetic optimization driven multi layer hybrid fuzzy neural networks. Simul Model Pract Theory 14: 597-613.
10. Park KJ, Pedrycz W, Oh SK (2007) A genetic approach to modeling fuzzy systems based on information granulation and successive generation-based evolution method. Simul Model Pract Theory 15: 1128-1145.
11. Koza YJR, Rice JP (1991) Genetic generation of both the weights and architecture for a neural network. In: IEEE international joint conference on neural networks, vol 2. IEEE Press, Seattle, pp 397-404.
12. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester.
13. Schaffer JD (2011) Some experiments in machine learning using vector evaluated genetic algorithm. Ph. D Thesis, Vanderbilt University, Nashville, TN.
14. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi-objective optimization: formulation, discussion and generalisation. In: Proceedings of the fifth international conference on genetic algorithms, Morgan Kaufman, San Mateo, pp 416-423.
15. Srinivas N, Deb K (1994) Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol Comput 2(3): 221-248.
16. Fieldsend JE, Singh S (2005) Pareto evolutionary neural networks. IEEE Trans Neural Netw 16(2): 338-354.
17. Abbass HA (2003) Speeding up backpropagation using multiobjective evolutionary algorithms. Neural Comput 15(11): 2705-2726.
18. Sexton RS, Dorsey RE, Sikander NA (2004) Simultaneous optimization of neural network function and architecture algorithm. Decis Support Syst 36: 283-296.
19. Gonzalez J, Rojas I, Ortega J, Pomares H, Fernandez J, Diaz A (2003) Multi-objective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation. IEEE Trans Neural Netw 14(1): 1478-1495.
20. Palmes S (2005) Robustness, evolvability and optimality in evolutionary neural networks. Biosystems 82(2): 168-188.
21. Mandal D, Pal SK, Sah P (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Process Technol 186: 154-162.
22. Sexton RS, Dorsey RE, Johnson JD (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114: 589-601.
23. Ljung L (1999) System identification, theory for the user, 2nd edn. Prentice-Hall, Englewood Cliffs.
24. Ibnkahla M (2003) Nonlinear system identification using neural networks trained with natural gradient descent. EURASIP J Appl Signal Process 12: 1229-1237.
25. Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2: 183-192.
26. Norgaard MRO, Poulsen NK, Hansen LK (2000) Neural networks for modeling and control of dynamic systems. A practitioner's handbook. Springer, London.
27. Zitzler E, Deb K, Thiele L (2000) Comparison of multi-objective evolutionary algorithm: empirical results. Evol Comput 8: 173-195.
28. Billings SA, Voon WSF (1986) Correlation based model validity tests for non-linear models. Int J Control 44(1): 235-244.
29. Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis forecasting and control. Prentice-Hall Inc, Englewood Cliffs.