Abstract
The adaptive probabilistic neural networks for classification task in situation of overlapping classes is proposed. This network is designed to solve data classification task when data are fed sequentially in the online mode, and forming classes are mutually overlapped - the fuzzy case. The distinct feature of the network is that the learning process of the pattern layer uses the sliding window. This allows us to keep the constant number of neurons in this layer. Another point of the learning process is the tuning ability of activation functions’ widespread parameters in online mode. The described advantage allows us to improve the classification quality. Last but not least is the ability to compute both the probability and membership levels of each observation to each of forming classes. The proposed adaptive probabilistic neural network with fuzzy interference is simple in numerical implementation and has high learning speed. The results of experiments confirmed the correctness of approach under consideration.
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References
Specht, D.F.: Probabilistic neural networks. Neural Netw. 3, 109–118 (1990)
Specht, D.F.: Probabilistic neural networks and polynomial ADALINE as complementary techniques to classification. IEEE Trans. Neural Netw. 1, 111–121 (1990)
Parzen, E.: On the estimation of a probability density function and the mode. Ann. Math. Stat. 33, 1065–1076 (1962)
Bodyanskiy, Ye., Gorshkov, Ye., Kolodyazhniy, V., Wernstedt, J.: A learning of probabilistic neural network with fuzzy inference. In: Proceedings of the Sixth International Conference on Artificial Neural Nets and Generic Algorithms, ICANNGA 2003, pp. 13–17. Springer, Wien (2003)
Bodyanskiy, Ye., Gorshkov, Ye., Kolodyazhniy, V.: Resource-allocating probabilistic neuro-fuzzy network. In: Proceedings on 2nd Conference of European Union Society for Fuzzy Logic and Technology (EUSFLAT 2003), Zittau, Germany, 10–12 September, pp. 392–395 (2003)
Zahirniak, D.R., Chapman, R., Rogers, S.K., Suter, B.W., Kabriski, M., Pyatti, V.: Pattern recognition using radial basis function network. In: Aerospace Application of Artificial Intelligence, Proceedings, Dayton, Ohio, pp. 249–260 (1990)
Yi, J.-H., Wang, J., Wang, G.-G.: Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem - advances. Mech. Eng. 8(1), 1–13 (2016)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1987)
Bodyanskiy, Ye., Gorshkov, Ye., Kolodyazhniy, V., Wernstedt, J.: Probabilistic neuro-fuzzy network with non-conventional activation functions. In: Lecture Notes in Artificial Intelligence, vol. 2773, pp. 973–979. Springer, Heidelberg (2003)
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Bodyanskiy, Y., Deineko, A., Pliss, I., Chala, O. (2021). Adaptive Learning of Probabilistic Neural Network in Situation of Overlapping Classes in Classification Task. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_24
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DOI: https://doi.org/10.1007/978-3-030-63270-0_24
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