String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization

  • Haza Nuzly Abdull Hamed
  • Nikola Kasabov
  • Zbynek Michlovský
  • Siti Mariyam Shamsuddin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5864)


This paper proposes a novel method for string pattern recognition using an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism optimizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features.


String Kernels Text Classification Evolving Spiking Neural Network Particle Swarm Quantum Computing 


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  1. 1.
    Kasabov, N.: Evolving Connectionist Systems: The System Engineering Approach, 2nd edn. Springer, New York (2007)zbMATHGoogle Scholar
  2. 2.
    Wysoski, S.G., Benuskova, L., Kasabov, N.: On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 61–70. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Hopfield, J.: Pattern Recognition Computation Using Action Potential Timing for Stimulus Representation. Nature 376, 33–36 (1995)CrossRefGoogle Scholar
  4. 4.
    Bohte, S.M., Kok, J.N., La Poutre, H.: Error-Backpropagation in Temporally Encoded Networks of Spiking Neurons. Neurocomputing 48(1) (2002)Google Scholar
  5. 5.
    Thorpe, S.J.: How Can The Human Visual System Process A Natural Scene in Under 150ms? Experiments and Neural Network Models. In: ESANN (1997)Google Scholar
  6. 6.
    Schliebs, S., Defoin-Platel, M., Kasabov, N.: Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network. In: Köppen, M., et al. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 1229–1236. Springer, Heidelberg (2009)Google Scholar
  7. 7.
    Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network: Exploring Heterogeneous Probabilistic Models. Neural Networks 22, 623–632 (2009)CrossRefGoogle Scholar
  8. 8.
    Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Press, NJ (1995)CrossRefGoogle Scholar
  9. 9.
    Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transactions on Evolutionary Computation 6, 580–593 (2002)CrossRefGoogle Scholar
  10. 10.
    Sun, J., Feng, B., Xu, W.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. Cong. Evolutionary Computation, CEC 2004, vol. 1, pp. 325–331 (2004)Google Scholar
  11. 11.
    Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. Automation and Remote Control 25, 821–837 (1964)MathSciNetGoogle Scholar
  12. 12.
    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text Classification Using String Kernels. Journal of Machine Learning Research 2, 419–444 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    UCI Machine Learning Repository,
  14. 14.
    Kasabov, N.: Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 3–13. Springer, Heidelberg (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Haza Nuzly Abdull Hamed
    • 1
  • Nikola Kasabov
    • 1
  • Zbynek Michlovský
    • 2
  • Siti Mariyam Shamsuddin
    • 3
  1. 1.Knowledge Engineering and Discovery Research Institute (KEDRI)Auckland University of TechnologyNew Zealand
  2. 2.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic
  3. 3.Soft Computing Research GroupUniversiti Teknologi MalaysiaMalaysia

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