Training Elman and Jordan networks for system identification using genetic algorithms

Artificial Intelligence in Engineering

Volumn 13, Issue 2, 1999, Pages 107-117

  Pham, D T ^a   Karaboga, D ^a  

^a CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Elman networks; Genetic algorithms; Jordan networks; Recurrent neural networks; System identification

Indexed keywords

ERROR ANALYSIS; FEEDBACK; FEEDFORWARD NEURAL NETWORKS; GENETIC ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); EFFICIENCY; FEEDBACK CONTROL; OPTIMIZATION;

ELMAN NETWORK; FEEDBACK CONNECTIONS; JORDAN NETWORKS;

RECURRENT NEURAL NETWORKS;

NETWORK TRAINING;

EID: 0033116077     PISSN: 09541810     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0954-1810(98)00013-2     Document Type: Article

References (15)

1. Pham DT, Liu X. Neural networks for identification, prediction and control. London: Springer-Verlag, 1995.
2. Yun L, Häußler A. Artificial evolution of neural networks and its application to feedback control. Artificial Intelligence in Engineering 1996;10(2):143-152.
3. Pham DT, Liu X. Training of Elman networks and dynamic system modelling. Int. Journal of Systems Science 1996;27(2):221-226.
4. Ku C-C, Lee KY. Diagonal recurrent neural networks for dynamic systems control. IEEE Trans. on Neural Networks 1995;6(1):144-156.
5. Pham DT, Liu X. Dynamic system identification using partially recurrent neural networks. Journal of Systems Engineering 1992;2(2):90-97.
6. Elman JL. Finding structure in time. Cognitive Science 1990;14:179-211.
7. Jordan MI. Attractor dynamics and parallelism in a connectionist sequential machine, Proc. Eighth Annual Conf. of the Cognitive Science Society, Amherst, MA, 1986, pp.531-546.
8. Pham DT, Oh SJ. A recurrent backpropagation neural network for dynamic system identification. Journal of Systems Engineering 1992;2(4):213-223.
9. Rumelhart DE, McClelland JL. Explorations in the Micro-Structure of Cognition. Parallel Distributed Processing, 1. Cambridge, MA: MIT Press, 1986.
10. Davis L. Handbook of Genetic Algorithms. New York, NY: Van Nostrand Reinhold, 1991.
11. Chalmers DJ. The evolution of learning: an experiment in genetic connectionism, In: Touretzky, D.S., Elman, J.L. and Hinton, G.E., (eds), Proc. 1990 Connectionist Models Summer School, 1990, pp.81-90, Morgan Kaufmann, San Mateo, CA.
12. Whitely D, Hanson T. Optimising neural networks using faster, more accurate genetic search, In: Schaffer, J.D., (ed), Proc. Third Int. Conf. on Genetic Algorithms and Their Applications, 1989, pp.370-374, Morgan Kaufmann, San Mateo, CA.
13. Harp SA, Samad T. Genetic synthesis of neural network architecture. In: Davis L, editor. Handbook of Genetic Algorithms, New York, NY: Van Nostrand Reinhold, 1991, pp.203-221.
14. Grefenstette JJ. Optimization of control parameters for genetic algorithms. IEEE Trans. on Systems Man and Cybernetics 1986;SMC-16(1):122-128.
15. Pham DT, Karaboga D. Optimum design of fuzzy logic controllers using genetic algorithms. Journal of Systems Engineering 1991;1(2):114-118.