Improved SpikeProp for using particle swarm optimization

Mathematical Problems in Engineering

Volumn 2013, Issue , 2013, Pages

  Ahmed, Falah Y H ^a   Shamsuddin, Siti Mariyam ^a   Hashim, Siti Zaiton Mohd ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX PATTERN; ERROR BACK-PROPAGATION; INPUT AND OUTPUTS; LEARNING ENHANCEMENTS; NONLINEAR CLASSIFICATION; SPIKING NETWORKS; SUPERVISED TRAININGS; TEMPORAL CODING;

BACKPROPAGATION ALGORITHMS; NEURAL NETWORKS; NEURONS; PARTICLE SWARM OPTIMIZATION (PSO); SUPERVISED LEARNING;

COMPLEX NETWORKS;

EID: 84885463428     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2013/257085     Document Type: Article

References (31)

1. Jin Y. J., Shen B. X., Ren R. F., Yang L., Sui J., Zhao J. G., Prediction of the styrene butadiene rubber performance by emulsion polymerization using backpropagation neural network. Journal of Engineering 2013 2013 6 515704 10.1155/2013/515704
2. Gerstner W., Kempter R., van Hemmen J. L., Wagner H., Hebbian Learning of Pulse Timing in the Barn Owl Auditory System in Maass 1999
3. Belatreche A., Paul R., Dynamic cluster formation using populations of spiking neurons. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN '12) June 2012 1 6 10.1109/IJCNN.2012.6252532
4. Ferster D., Spruston N., Cracking the neuronal code. Science 1995 270 5237 756 757 2-s2.0-0028783772 10.1126/science.270.5237.756
5. Thorpe S., Delorme A., van Rullen R., Spike-based strategies for rapid processing. Neural Networks 2001 14 6-7 715 725 10.1016/S0893-6080(01)00083-1 (Pubitemid 33035869)
6. Kasabov N., Evolving, probabilistic spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition. 7311 Proceedings of the IEEE World Congress on Computational Intelligence (WCCI '12) 2012 234 260 Lecture Notes in Computer Science
7. Bishop C. M., Neural Networks for Pattern Recognition 2000
8. Haykin S., Neural Networks: A Comprehensive Foundation 1998 Prentice Hall PTR
9. Negnevitsky M., Artificial Intelligence: A Guide to Intelligent Systems 2002 Addison Wesley
10. Kasabov N., Integrative connectionist learning systems inspired by nature: current models, future trends and challenges. Natural Computing 2009 8 2 199 218 2-s2.0-67349139169 10.1007/s11047-008-9066-z MR2505749
11. Gewaltig, Evolution of Synchronous Spike Volleys in Cortical Networks: Network Simulations and Continuous Probabilistic Models 2000 1822 Springer Lecture Notes in Artificial Intelligence
12. Thorpe S., Gaustrais J., Rank order coding. Computational Neuroscience: Trends in Research 1998 New York, NY, USA Plenum Press 113 118
13. Kasabov N., To spike or not to spike: probabilistic spiking neuron model. Neural Networks 2010 23 1 16 19 2-s2.0-70649084995 10.1016/j.neunet.2009.08.010
14. Maass W., Maass W., Bishop C., Computing with spiking neurons. Pulsed Neural Networks 2001 2 Cambridge, Mass, USA MIT Press 55 85
15. Mishra D., Yadav A., Dwivedi A., Kalra P. K., A neural network using single multiplicative spiking neuron for function approximation and classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN '06) July 2006 396 403 2-s2.0-34547317714
16. Grüning A., Sporea I., Supervised learning of logical operations in layered spiking neural networks with spike train encoding. Neural Processing Letters 2012 36 2 117 134 10.1007/s11063-012-9225-1
17. Gerstner W., Kistler W. M., Spiking Neuron Models 2002 Cambridge University Press
18. Bohte S. M., Kok J. N., La Poutré H., Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 2002 48 1-4 17 37 2-s2.0-0036826068 10.1016/S0925-2312(01)00658-0 (Pubitemid 36221486)
19. Šma J., Gradient learning in networks of smoothly spiking neurons. 2009 1045
20. Rumelhart D. E., Hinton G. E., Williams R. J., Learning representations by back-propagating errors. Nature 1986 323 6088 533 536 2-s2.0-0022471098 (Pubitemid 16025374)
21. Gerstner W., Kistler W. M., Spiking Neuron Models: An Introduction 2002 Cambridge University Press
22. Moore S. C., Back-propagation in spiking neural networks [M.S. thesis] 2002 Bath, UK Department of Computer Science, University of Bath
23. Xin J., Embrechts M. J., Supervised learning with spiking neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN '01) July 2001 1772 1777 2-s2.0-0034862761
24. Schrauwen B., Campenhout J. V., Verleysen M., Parallel hardware implementation of a broad class of spiking neurons using serial arithmetic. Proceedings of the 14th European Symposium on Artificial Neural Networks 2007 623 628
25. Berkovec K., Learning in networks of spiking neurons [M.S. thesis] 2006 Prague, Czech Republic Faculty of Mathematics and Physics, Charles University
26. Tiňo P., Mills A. J. S., Learning beyond finite memory in recurrent networks of spiking neurons. Neural Computation 2006 18 3 591 613 2-s2.0-33645701621 10.1162/089976606775623360 MR2200944 ZBL1087.92012 (Pubitemid 43543834)
27. Fiete I. R., Seung H. S., Gradient learning in spiking neural networks by dynamic perturbation of conductances. Physical Review Letters 2006 97 4 2-s2.0-33746652644 10.1103/PhysRevLett.97.048104 048104 (Pubitemid 44147303)
28. Mergenthaler K., Engbert R., Modeling the control of fixational eye movements with neurophysiological delays. Physical Review Letters 2007 98 13 2-s2.0-34047189124 10.1103/PhysRevLett.98.138104 138104 (Pubitemid 46515830)
29. Nakazawa K., Quirk M. C., Chitwood R. A., Watanabe M., Yeckel M. F., Sun L. D., Kato A., Carr C. A., Johnston D., Wilson M. A., Tonegawa S., Requirement for hippocampal CA3 NMDA receptors in associative memory recall. Science 2002 297 5579 211 218 2-s2.0-0037067583 10.1126/science.1071795 (Pubitemid 34761276)
30. Shi Y., Particle Swarm Optimization 2004 IEEE neural Network Society
31. Chan L. W., Fallside F., An adaptive training algorithm for back propagation networks. Computer Speech and Language 1987 2 3-4 205 218 2-s2.0-0001031887