Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task
Abstract
The probabilistic neuro-fuzzy system to solve the image classification-recognition task is proposed. The considered system is a “hybrid” of Specht’s probabilistic neural network and the neuro-fuzzy system of Takagi-Sugeno-Kang. It is designed to solve tasks in case of overlapping classes. Also, it is supposed that the initial data that are fed on the input of the system can be represented in numerical, rank, and nominal (binary) scales. The tuning of the network is implemented with the modified procedure of lazy learning based on the concept “neurons at data points”. Such a learning approach allows substantially reducing the consumption of time and does not require large amounts of training dataset. The proposed system is easy in computational implementation and characterised by a high classification speed, as well as allows processing information both in batch and online mode.
Keywords: |
Lazy learning; membership function; neural network; neuro-fuzzy system; probabilistic pattern recognition
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References
D. F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, no. 1, pp. 109–118, 1990. https://doi.org/10.1016/0893-6080(90)90049-Q
D. F. Specht, “Probabilistic neural networks and polynomial ADALINE as complementary techniques to classification”, IEEE Trans. on Neural Networks, vol. 1, pp. 111–121, 1990. https://doi.org/10.1109/72.80210
Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy and J. Wernstedt, “A learning of probabilistic neural network with fuzzy inference,” in Pearson D.W., Steele N.C., Albrecht R.F. (eds), Artificial Neural Nets and Genetic Algorithms, Springer, Vienna, 2003, pp. 3–17. https://doi.org/10.1007/978-3-7091-0646-4_3
Ye. Bodyanskiy, Ye. Gorshkov and V. Kolodyazhniy, “Resource-Allocating Probabilistic Neuro-Fuzzy Network,” Proc. 2nd Conf. of European Union Sosciety for Fuzzy Logic and Technology (EUSFLAT 2003), Zittau, Germany, 10–12 September 2003, pp. 392–395.
Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, J. Wernstedt, “Probabilistic neuro-fuzzy network with non-conventional activation functions,” in: Palade V., Howlett R.J., Jain L. (eds), Knowledge-Based Intelligent Information and Engineering Systems, KES 2003. Lecture Notes in Artificial Intelligence, vol. 2773, Berlin, Heidelberg, New York, Springer, 2003, pp. 973–979. https://doi.org/10.1007/978-3-540-45226-3_133
L. Rutkowski, “Adaptive probabilistic neural networks for pattern classification in time-varying environment,” IEEE Trans. on Neural Networks, vol. 5, no. 4, pp. 811–827, 2004. https://doi.org/10.1109/TNN.2004.828757
Ye. Bodyanskiy and O. Shubkina, “Semantic annotation of text documents using modified probabilistic neural network,” 6th IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Prague, Czech Republic, 2011, pp. 328–331. https://doi.org/10.1109/IDAACS.2011.6072767
Ye. Bodyanskiy, O. Shubkina, “Semantic annotation of text documents using evolving neural network based on principle “Neurons at Data Points” 4th Int. Workshop on Inductive Modeling “IWIM 2011”, Kyiv, Ukraine, 2011, pp. 31–37. https://doi.org/10.1109/IDAACS.2011.6072767
Ye. Bodyanskiy, I. Pliss and V. Volkova, “Modified probabilistic neuro-fuzzy network for text document processing,” Int. J. Computing, vol. 11, no. 4, 2012, pp. 391–396.
J.-H. Yi, J. Wang, G.-G. Wang, “Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem,” Advances in Mechanical Engineering, vol. 8, no. 1, 2016, pp. 1–13. https://doi.org/10.1177/1687814015624832
P. Zhernova, I. Pliss, O. Chala “Modified fuzzy probabilistic neural network,” Intellectual Systems for Decision Making and Problems of Computational Intelligence ISDMCI’2018, Kherson, PP Vyshemirsky V. S., pp. 228–230, 2018.
J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. N.Y.: Plenum Press, 1987.
C. Mumford, L. Jain (Eds), Computational Intelligence, Collaboration, Fuzzy and Emergence. Berlin, Springer - Verlag, 2009. https://doi.org/10.1007/978-3-642-01799-5
R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher, P. Held, Computational Intelligence. A Methodological Introduction. Berlin: Springer-Verlag, 2013. https://doi.org/10.1007/978-1-4471-5013-8
J. Kacprzyk, W. Pedrycz, Springer Handbook of Computational Intelligence. Berlin, Heidelberg, Springer-Verlag, 2015. https://doi.org/10.1007/978-3-662-43505-2
P.V.C. Souza, “Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature,” Applied Soft Computing, vol. 92, July 2020. https://doi.org/10.1016/j.asoc.2020.106275
D.R. Zahirniak, R. Chapman, S.K. Rogers, B.W. Suter, M. Kabriski, V. Pyatti, “Pattern recognition using radial basis function network,” Proceedings Aerospace Application of Artificial Intelligence. Dayton, Ohio, 1990, pp. 249–260.
O. Nelles, Nonlinear Systems Identification. Berlin, Springer, 2001. https://doi.org/10.1007/978-3-662-04323-3
Y. Bodyanskiy, A. Deineko, I. Pliss, O. Chala “Evolving fuzzy-probabilistic neural network and its online learning” 2020 10th International Conference on Advanced Computer Information Technologies. Deggendorf, Germany, 16–18 September, 2020, in Press. https://doi.org/10.1109/ACIT49673.2020.9208904
DOI: 10.7250/itms-2020-0002
Cited-By
1. Matrix Neo-Fuzzy-System and its Online Learning in Image Recognition Task
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Information Technology and Management Science vol: 24 first page: 39 year: 2021
doi: 10.7250/itms-2021-0006
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