Quantum-inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2009, Pages 2833-2840

  Schliebs, Stefan ^a   Platel, Michaël Defoin ^c   Worner, Sue ^b   Kasabov, Nikola ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^b LINCOLN UNIVERSITY   (New Zealand)

^c ROTHAMSTED RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN CLASSIFIER; CLASSIFICATION ACCURACY; CONNECTIONIST SYSTEMS; ECOLOGICAL DATA; ECOLOGICAL MODELING; FEATURE SUBSET; OPTIMISATIONS; PARAMETER OPTIMISATION; QUANTUM INSPIRED EVOLUTIONARY ALGORITHM; SPIKING NEURAL NETWORKS;

EVOLUTIONARY ALGORITHMS; INTELLIGENT AGENTS;

NEURAL NETWORKS;

EID: 68149182835     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2009.5179049     Document Type: Conference Paper

References (27)

1. W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge, MA: Cambridge University Press, 2002. [Online]. Available: http://www.eur.n1/fgg/neuro/homepages/W. M. Kistler.htm
2. E. M. Izhikevich, "Simple model of spiking neurons, " IEEE Trans, on Neural Networks, vol. 14, no. 6, pp. 1569-1572, 2003.
3. N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2007.
4. S. G. Wysoski, L. Benuskova, and N. Kasabov, "On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition, " in ICANN (1), 2006, pp. 61-70.
5. -, "Fast and adaptive network of spiking neurons for multi-view visual pattern recognition, " Neurocomputing, vol. 71, no. 13-15, pp. 2563-2575, 2008.
6. S. Soltic, S. Wysoski, and N. Kasabov, "Evolving spiking neural networks for taste recognition, " in IEEE World Congress on Computational Intelligence (WCCI), Hong Kong, 2008.
7. D. Verstraeten, B. Schrauwen, and D. Stroobandt, "Isolated word recognition using a liquid state machine, " in ESANN, 2005, pp. 435-440.
8. A. Knoblauch, "Neural associative memory for brain modeling and information retrieval, " Inf. Process. Lett., vol. 95, no. 6, pp. 537-544, 2005.
9. N. Iannella and L. Kindermann, "Finding iterative roots with a spiking neural network, " Information Processing Letters, vol. 95, no. 6, pp. 545-551, 2005, applications of Spiking Neural Networks.
10. S. Schliebs, M. Defoin-Platel, and N. Kasabov, "Integrated feature and parameter optimization for an evolving spiking neural network, " in IEEE International Conference on Neural Information Processing, 2009, in Print.
11. M. Defoin Platel, S. Schliebs, and N. Kasabov, "A versatile quantuminspired evolutionary algorithm, " in IEEE Congress on Evolutionary Computation, 2007. CEC'07, 2007, pp. 423-430.
12. -, "Quantum-inspired evolutionary algorithm: A multimodel eda, " Evolutionary Computation, IEEE Transactions on, 2009, in print.
13. S. Worner, G. Lankin, S. Samarasinghe, and D. Teulon, "Improving prediction of aphid flights by temporal analysis of input data for an artificial neural network, " New Zealand Plant Protection, vol. 55, pp. 312-316, 2002.
14. M. Watts and S. Worner, "Using mlp to determine abiotic factors influencing the establishment of insect pest species, " in International Joint Conference on Neural Networks (IJCNN '06). Vancouver, Canada: IEEE, 2006, pp. 1840-1845.
15. N. Cocu, R. Harrington, M. Rounsevell, S. Worner, and M. Hulle, "Geographical location, climate and land use influences on the phenology and numbers of the aphid, myzus persicae, in europe, " Journal of Biogeography, vol. 32, no. 4, pp. 615-632, 2005.
16. L. Benuskova and N. Kasabov, Computational Neurogenetic Modelling. NY: Springer, 2007.
17. R. VanRullen and S. J. Thorpe, "Is it a bird? is it a plan? ultrarapid visual categorisation of natural and artificial objects, " Perception, vol. 30, pp. 655-668, 2001.
18. S. J. Thorpe, "How can the human visual system process a natural scene in under 150ms? experiments and neural network models, " in ESANN, 1997.
19. S. G. Wysoski, "Evolving spiking neural networks for adaptive audiovisual pattern recognition, " Ph. D. dissertation, Auckland University of Technology, August 2008, http://hdl.handle.net/10292/390.
20. S. M. Bohte, J. N. Kok, and J. A. L. Poutré, "Error- backpropagation in temporally encoded networks of spiking neurons, " Neurocomputing, vol. 48, no. 1-4, pp. 17-37, 2002.
21. M. Valko, N. C. Marques, and M. Castelani, "Evolutionary feature selection for spiking neural network pattern classifiers, " in Proceedings of 2005 Portuguese Conference on Artificial Intelligence, B. et al., Ed. IEEE, 2005, pp. 24-32.
22. "Cabi (2003), crop protection compendium, global module, 5th edition, " Wallingford, UK, 2003.
23. E. Cantu-Paz, "Feature subset selection by estimation of distribution algorithms, " in GECCO '02: Proceedings of the Genetic and Evolutionary Computation Conference. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2002, pp. 303-310.
24. S. Worner, G. Leday, and T. Ikeda, "Uncertainty analysis and ensemble selection of statistical and machine learning models that predict species distribution, " in Ecological Informatics, Cancun, Mexico, 2008.
25. M. T. Vera, R. Rodriguez, D. F. Segura, J. L. Cladera, and R. W. Sutherst, "Potential geographical distribution of the mediterranean fuit fly, ceratitis capitata (diptera:tephritidae) with emphasis on argentina and australia, " Environmental entomology, vol. 31, no. 6, pp. 1009-1022, 2002.
26. N. Kasabov, "Integrative connectionist learning systems inspired by nature: Current models, future trends and challenges, " Natural Computing, no. Jan, 2008.
27. -, "Evolving intelligence in humans and machines: Integrative connectionist systems approach (feature article), " IEEE Computational Intelligence Magazine, vol. 3, no. 3, pp. 23-37, 2008.