PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Single spiking neuron multi-objective optimization for pattern classification

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As neuron models become more plausible, fewer computing units may be required to solve some problems; such as static pattern classification. Herein, this problem is solved by using a single spiking neuron with rate coding scheme. The spiking neuron is trained by a variant of Multi-objective Particle Swarm Optimization algorithm known as OMOPSO. There were carried out two kind of experiments: the first one deals with neuron trained by maximizing the inter distance of mean firing rates among classes and minimizing standard deviation of the intra firing rate of each class; the second one deals with dimension reduction of input vector besides of neuron training. The results of two kind of experiments are statistically analyzed and compared again a Mono-objective optimization version which uses a fitness function as a weighted sum of objectives.
Twórcy
  • Postgraduate Studies and Research Division, Leon Institute of Technology – National Technology of Mexico, Leon, Guanajuato, Mexico
  • Postgraduate Studies and Research Division, Leon Institute of Technology – National Technology of Mexico, Leon, Guanajuato, Mexico
  • Department of Organizational Studies, DCEA-University of Guanajuato, Guanajuato, Mexico
  • Postgraduate Studies and Research Division, Leon Institute of Technology – National Technology of Mexico, Leon, Guanajuato, Mexico
  • Postgraduate Studies and Research Division, Leon Institute of Technology – National Technology of Mexico, Leon, Guanajuato, Mexico
  • Department of Organizational Studies, DCEA-University of Guanajuato, Guanajuato, Mexico
  • Department of Electronics, DICIS-University of Guanajuato, Salamanca, Guanajuato, Mexico
Bibliografia
  • [1] M. van Gerven and S. Bohte, “Editorial: Artificial Neural Networks as Models of Neural Information Processing”, Frontiers in Computational Neuroscience, vol. 11, 2017, 1–2, DOI: 10.3389/fncom.2017.00114.
  • [2] K. Soltanian, F. A. Tab, F. A. Zar and I. Tsoulos, “Artificial neural networks generation using grammatical evolution”. In: 2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013, 1–5, DOI: 10.1109/IranianCEE.2013.6599788.
  • [3] W. Maass, “Networks of spiking neurons: The third generation of neural network models”, Neural Networks, vol. 10, no. 9, 1997, 1659–1671, DOI: 10.1016/S0893-6080(97)00011-7.
  • [4] D. Gardner, The Neurobiology of neural networks, MIT Press, 1993.
  • [5] N. G. Pavlidis, O. K. Tasoulis, V. P. Plagianakos, G. Nikiforidis and M. N. Vrahatis, “Spiking neural network training using evolutionary algorithms”. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 4, 2005, 2190–2194, DOI: 10.1109/IJCNN.2005.1556240.
  • [6] A. A. Hopgood, Intelligent Systems for Engineers and Scientists, CRC Press/Taylor & Francis Group, 2012.
  • [7] B. A. Garro and R. A. Vázquez, “Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms”, Computational Intelligence and Neuroscience, 2015, 1–20, DOI: 10.1155/2015/369298.
  • [8] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning internal representations by error propagation”. In: Parallel distributed processing: explorations in the microstructure of cognition, vol. 1, MIT Press, 1986, 318–362.
  • [9] D. Karaboga, B. Akay and C. Ozturk, “Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks”. In: V. Torra, Y. Narukawa and Y. Yoshida (eds.), Modeling Decisions for Artificial Intelligence, vol. 4617, 2007, 318–329, DOI: 10.1007/978-3-540-73729-2_30.
  • [10] S. Ding, H. Li, C. Su, J. Yu and F. Jin, “Evolutionary artificial neural networks: a review”, Artificial Intelligence Review, vol. 39, no. 3, 2013, 251–260, DOI: 10.1007/s10462-011-9270-6.
  • [11] R. A. Vazquez and A. Cachon, “Integrate and Fire neurons and their application in pattern recognition”. In: 2010 7th International Conference on Electrical Engineering Computing Science and Automatic Control, 2010, 424–428, DOI: 10.1109/ICEEE.2010.5608622.
  • [12] A. Cachón and R. A. Vázquez, “Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems”, Neurocomputing, vol. 148, 2015, 187–197, DOI: 10.1016/j.neucom.2012.11.059.
  • [13] E. M. Izhikevich, “Which Model to Use for Cortical Spiking Neurons?”, IEEE Transactions on Neural Networks, vol. 15, no. 5, 2004, 1063–1070, DOI: 10.1109/TNN.2004.832719.
  • [14] C. A. Coello Coello and M. S. Lechuga, “MOPSO: a proposal for multiple objective particle swarm optimization”. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02, vol. 2, 2002, 1051–1056, DOI: 10.1109/CEC.2002.1004388.
  • [15] M. R. Sierra and C. A. Coello Coello, “Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance”. In: C. A. Coello Coello, A. Hernández Aguirre and E. Zitzler (eds.), Evolutionary Multi-Criterion Optimization, vol. 3410, 2005, 505–519, DOI: 10.1007/978-3-540-31880-4_35.
  • [16] J. J. Durillo, A. J. Nebro and E. Alba, “The jMetal framework for multi-objective optimization: Design and architecture”. In: IEEE Congress on Evolutionary Computation, 2010, 1–8, DOI: 10.1109/CEC.2010.5586354.
  • [17] J. J. Durillo and A. J. Nebro, “jMetal: A Java framework for multi-objective optimization”, Advances in Engineering Software, vol. 42, no. 10, 2011, 760–771, DOI: 10.1016/j.advengsoft.2011.05.014.
  • [18] “UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science”. D. Dua and C. Graff, http://archive.ics.uci.edu/ml. Accessed on: 2020-05-28.
  • [19] J. Derrac, S. García, D. Molina and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and Evolutionary Computation, vol. 1, no. 1, 2011, 3–18, DOI: 10.1016/j.swevo.2011.02.002.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be906d95-5606-49fd-bcfe-7025843dbfa7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.