Abstract
In this paper a generalized net model of the Neocognitron neural network is presented. A Network Neocognitron is a self-organizing network with the ability to recognize patterns based on the difference of their form. A neocognitron is able to correctly identify an image, even if there is a violation or movement into position. Self-organization in the neocognitron is also realized uncontrollably - training for self-organizing neocognitron takes only a collection of recurring patterns in the recognizable image and does not need the information for categories that include templates. The output producing process is presented by a Generalized net model.
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References
Atanassov, K.: Generalized nets as a tool for the modelling of data mining processes. In: Sgurev, V., Yager, Ronald R., Kacprzyk, J., Jotsov, V. (eds.) Innovative Issues in Intelligent Systems. SCI, vol. 623, pp. 161–215. Springer, Cham (2016). doi:10.1007/978-3-319-27267-2_6
Atanassov, K.: Generalized Nets. World Scientific, Singapore (1991)
Atanassov, K.: On Generalized Nets Theory. “Prof. Marin Drinov”Academic Publishing House, Sofia (2007)
Atanassov, K., Sotirov, S. Antonov, A.: Generalized net model for parallel optimization of feed-forward neural network. Adv. Stud. Contemp. Math. 15(1), 109–119 (2007)
Atanassov, K., Sotirov S.: Optimization of a neural network of self-organizing maps type with time-limits by a generalized net. Adv. Stud. Contemp. Math. 13(2), 213–220 (2006)
Bureva, V., Sotirova, E., Atanassov, K.: Hierarchical generalized net model of the process of selecting a method for clustering. In: 15th International Workshop on Generalized Nets Burgas, 16 October 2014, pp. 39–48 (2006)
Bureva, V., Sotirova, E., Atanassov, K.: Hierarchical generalized net model of the process of clustering, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, vol. 1, Warsaw School of Information Technology, pp. 73–80 (2014)
Bureva, V.: Intuitionistic fuzzy histograms in grid-based clustering. Notes Intuitionistic Fuzzy Sets 20O(1), 55–62 (2014)
Bureva, V., Sotirova, E., Chountas, P.: Generalized net of the process of sequential pattern mining by generalized sequential pattern algorithm (GSP). In: Filev, D., et al. (eds.) Intelligent Systems’2014. AISC, vol. 323, pp. 831–838. Springer, Cham (2015). doi:10.1007/978-3-319-11310-4_72
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
Fukushima, K.: Restoring partly occluded patterns: a neural network model. Neural Netw. 18(1), 33–43 (2005)
Hagan, M., Demuth, H., Beale, M.: Neural Network Toolbox 7 (2010)
Krawczak, M.: Generalized Net Models of Systems, Bulletin of Polish Academy of Science (2003)
Sotirov, S.: Generalized net model of the Time Delay Neural Network, Issues in Intuitionistic Fuzzy Sets and Generalized nets, Warsaw, 2010, pp. 125–131 (2010)
Sotirov, S.: Modeling the algorithm Backpropagation for training of neural networks with generalized nets – part 1. In: Proceedings of the Fourth International Workshop on Generalized Nets, Sofia, 23 September 2003, pp. 61–67 (2003)
Sotirov, S.: Generalized net model of the accelerating backpropagation algorithm. Jangjeon Math. Soc. 2006, 217–225 (2006)
Sotirov, S., Krawczak, M.: Modeling the algorithm Backpropagation for learning of neural networks with generalized nets – part 2. Issues in Intuitionistic Fuzzy Sets Generalized Nets, Warszawa, pp. 65–70 (2007)
Pencheva, T., Roeva, O., Shannon, A.: Generalized net models of basic genetic algorithm operators. In: Angelov, P., Sotirov, S. (eds.) Imprecision and Uncertainty in Information Representation and Processing. SFSC, vol. 332, pp. 305–325. Springer, Cham (2016). doi:10.1007/978-3-319-26302-1_19
Roeva, O., Atanassova, V.: Generalized net model of Cuckoo search algorithm. In: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings, 2016, pp. 589–592 (2016)
Roeva, O., Shannon, A., Pencheva, T., Description of simple genetic algorithm modifications using Generalized Nets. In: IS 2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings, pp. 178–183 (2012)
Ribagin, S., Chakarov, V., Atanassov, K.: Generalized net model of the scapulohumeral rhythm. In: Sgurev, V., Yager, Ronald R., Kacprzyk, J., Atanassov, Krassimir T. (eds.) Recent Contributions in Intelligent Systems. SCI, vol. 657, pp. 229–247. Springer, Cham (2017). doi:10.1007/978-3-319-41438-6_13
Ribagin, S., Roeva, O., Pencheva, T.: Generalized Net model of asymptomatic osteoporosis diagnosing. In: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 – Proceedings, 7 November 2016, pp. 604–608 (2016)
Ribagin, S.: Generalized net model of age-associated changes in the upper limb musculoskeletal structures. Comptes Rendus de L’Academie Bulgare des Sciences 67(11), 1503–1512 (2014)
Ribagin, S., Chakarov, V., Atanassov, K.: Generalized net model of the upper limb vascular system. In: IS 2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings, pp. 229–232 (2012)
Acknowledgment
The authors are grateful for the support provided by the project DN-02/10 - “New Instruments for Knowledge Discovery from Data, and their Modelling”, funded by the National Science Fund, Bulgarian Ministry of Education, Youth and Science.
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Petkov, T., Jovcheva, P., Tomov, Z., Simeonov, S., Sotirov, S. (2017). A Generalized Net Model of the Neocognitron Neural Network. In: Christiansen, H., Jaudoin, H., Chountas, P., Andreasen, T., Legind Larsen, H. (eds) Flexible Query Answering Systems. FQAS 2017. Lecture Notes in Computer Science(), vol 10333. Springer, Cham. https://doi.org/10.1007/978-3-319-59692-1_22
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