Tuning the structure and parameters of a neural network using cooperative binary-real particle swarm optimization

Expert Systems with Applications

Volumn 38, Issue 5, 2011, Pages 4972-4977

  Zhao, Liang ^a   Qian, Feng ^a  

^a School of Information Science and Engineering   (China)

Author keywords

Cooperative; Neural network; Particle swarm optimization

Indexed keywords

COMPACT STRUCTURES; COOPERATIVE; HIDDEN NODES; LEARNING ABILITIES; OPTIMAL PARAMETER; PARTICLE SWARM; PARTICLE SWARM OPTIMIZATION ALGORITHM; SIMULATION EXPERIMENTS;

PARTICLE SWARM OPTIMIZATION (PSO); SHAPE OPTIMIZATION; STRUCTURAL OPTIMIZATION;

NEURAL NETWORKS;

EID: 79151477701     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.09.154     Document Type: Article

References (26)

1. F.V.D. Bergh, and A.P. Engelbrecht A cooperative approach to particle swarm optimization IEEE Transactions on Evolutioanry Computation 8 2004 225 239
2. M. Brown, and C. Harris Neural fuzzy adaptive modeling and control 1994 Prentice-Hall Englewood Cliffs, NJ
3. S.B. Cho, and K. Shimohara Evolutionary learning of modular neural networks with genetic programming Applied Intelligence 9 1998 191C200
4. Eberhart, R. C.; & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan (pp. 39-43).
5. M. El-Abd, and M. Kamel Cooperative particle swarm optimizers: A powerful and promising approach Studies in Computational Intelligence 31 2006 239 259
6. N. Garcia-Pedrajas, C. Hervas-Martinez, and J. Munoz-Perez COVNET: A cooperative coevolutionary model for evolving artificial neural networks IEEE Transaction on Neural Networks 14 2003 575 596
7. K. Hornick, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Networks 2 1989 359 366
8. S.H. Hsieh, and C.H. Chen Adaptive image interpolation using probabilistic neural network Expert Systems with Applications 36 2009 6025 6029
9. C.F. Juang A hybrid genetic algorithm and particle swarm optimization for recurrent network design IEEE Transactions on Systems, Man and Cybernetics 32 2004 997 1006
10. Kennedy, J.; & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks, Perth, Australia (pp. 1942-1948).
11. Kennedy, J.; & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the world multiconference on systemics, cybernetics and informatics, Piscataway, NJ (pp. 4104-4108).
12. F.H.F. Leung, H.K. Lam, S.H. Ling, and P.K.S. Tam Tuning of the structure and parameters of a neural network using an improved genetic algorithm IEEE Transaction on Neural Networks 14 2003 79 88
13. J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Transactions on Evolutionary Computation 10 2006 281 295
14. C.J. Lin, J.G. Wang, and C.Y. Lee Pattern recognition using neural-fuzzy networks based on improved particle swam optimization Expert Systems with Applications 36 2009 5402 5410
15. W.Z. Lu, H.Y. Fan, and S.M. Lo Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong Neurocomputing 51 2003 387 400
16. M. Mandischer A comparison of evolution strategies and backpropagation for neural network training Neurocomputing 42 2002 87 117
17. R. Mendes, J. Kennedy, and J. Neves The fully informed particle swarm: Simpler, maybe better IEEE Transactions on Evolutionary Computation 8 2004 204 210
18. M.A. Potter, and K.A. De Jong A cooperative coevolutionary approach to function optimization The third parallel problem solving from nature 1994 Springer-Verlag Berlin
19. Shi, Y.; & Eberhart, R. C. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE congress on evolutionary computation, Piscataway, USA.
20. Shi, Y.; & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Proceeding of the IEEE congress on evolutionary computation, Piscataway, USA.
21. Y. Song, Z.Q. Chen, and Z.Z. Yuan New Chaotic PSO-based neural network predictive control for nonlinear process IEEE Transaction on Neural Networks 18 2007 595 603
22. J.T. Tsai, J.H. Chou, and T.K. Liu Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm IEEE Transaction on Neural Networks 17 2006 69 80
23. X. Yao Evolving artificial neural networks Proceeding of IEEE 87 1999 1423 1447
24. J.B. Yu, S.J. Wang, and L.F. Xi Evolving artificial neural networks using an improved PSO and DPSO Neurocomputing 71 2008 1054 1060
25. L. Zhao, and Y.P. Yang PSO-based single multiplicative neuron for time serial prediction Expert Systems with Applications 36 2008 2805 2812
26. L. Zhao, Y.U. Yang, and Y. Zeng Eliciting compact T-S fuzzy models using subtractive clustering and coevolutionary particle swarm optimization Neurocomputing 72 2009 2569 2575