[
Basha, S.S., Dubey, S.R., Pulabaigari, V. and Mukherjee, S. (2020). Impact of fully connected layers on performance of convolutional neural networks for image classification, Neurocomputing 378: 112–119.10.1016/j.neucom.2019.10.008
]Search in Google Scholar
[
Bodyanskiy, Y.V. and Tyshchenko, O.K. (2019). A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning, International Journal of Applied Mathematics and Computer Science 29(3): 477–488, DOI: 10.2478/amcs-2019-0035.10.2478/amcs-2019-0035
]Search in Google Scholar
[
Carvalho, M. and Ludermir, T.B. (2006). Particle swarm optimization of feed-forward neural networks with weight decay, 6th International Conference on Hybrid Intelligent Systems (HIS’06), Rio de Janeiro, Brazil, pp. 5–5.
]Search in Google Scholar
[
Chang, C.-H. (2015). Deep and shallow architecture of multilayer neural networks, IEEE Transactions on Neural Networks and Learning Systems 26(10): 2477–2486.10.1109/TNNLS.2014.2387439
]Search in Google Scholar
[
Chen, G. (2010). Simplified particle swarm optimization algorithm based on particles classification, 6th International Conference on Natural Computation, Yantai, China, Vol. 5, pp. 2701–2705.
]Search in Google Scholar
[
Chen, M. (2008). Second generation particle swarm optimization, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China, pp. 90–96.
]Search in Google Scholar
[
Eberhart, R.C. and Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization, Proceedings of the 2000 Congress on Evolutionary Computation, CEC00, La Jolla, USA, Vol. 1, pp. 84–88.
]Search in Google Scholar
[
Gal, L., Botzheim, J. and Koczy, L.T. (2008). Improvements to the bacterial memetic algorithm used for fuzzy rule base extraction, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Istanbul, Turkey, pp. 38–43.
]Search in Google Scholar
[
Gál, L., Botzheim, J., Kóczy, L.T. and Ruano, A.E. (2009). Applying bacterial memetic algorithm for training feedforward and fuzzy flip-flop based neural networks, Joint 2009 International Fuzzy Systems Association World Congress and the European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal, pp. 1833–1838.
]Search in Google Scholar
[
Gál, L., Lovassy, R. and Kóczy, L.T. (2010). Function approximation performance of fuzzy neural networks based on frequently used fuzzy operations and a pair of new trigonometric norms, International Conference on Fuzzy Systems, Barcelona, Spain, pp. 1–8.
]Search in Google Scholar
[
Gniewek, L. and Kluska, J. (2004). Hardware implementation of fuzzy Petri net as a controller, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 34(3): 1315–1324.10.1109/TSMCB.2003.822956
]Search in Google Scholar
[
Hirota, K. and Ozawa, K. (1989). The concept of fuzzy flip-flop, IEEE Transactions on Systems, Man, and Cybernetics 19(5): 980–997.10.1109/21.44013
]Search in Google Scholar
[
Hirota, K. and Pedrycz, W. (1993). Neurocomputations with fuzzy flip-flops, Proceedings of International Conference on Neural Networks (IJCNN-93), Nagoya, Japan, Vol. 2, pp. 1867–1870.
]Search in Google Scholar
[
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization, Proceedings of the ICNN’95 International Conference on Neural Networks, Perth, Australia, Vol. 4, pp. 1942–1948.
]Search in Google Scholar
[
Kowalski, P.A. (2013). Evolutionary strategy for the fuzzy flip-flop neural networks supervised learning procedure, International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, pp. 294–305.
]Search in Google Scholar
[
Lillicrap, T.P., Cownden, D., Tweed, D.B. and Akerman, C.J. (2016). Random synaptic feedback weights support error backpropagation for deep learning, Nature Communications 7(1): 1–10.10.1038/ncomms13276
]Search in Google Scholar
[
Lovassy, R., Kóczy, L.T. and Gál, L. (2008a). Applicability of fuzzy flip-flops in the implementation of neural networks, 9th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2008, Budapest, Hungary, pp. 333–344.
]Search in Google Scholar
[
Lovassy, R., Koczy, L.T. and Gal, L. (2008b). Multilayer perceptron implemented by fuzzy flip-flops, IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Hong, Kong, China, pp. 1683–1688.10.1109/FUZZY.2008.4630597
]Search in Google Scholar
[
Lovassy, R., Zavala, A.H., Gál, L., Nieto, O.C., Kóczy, L.T. and Batyrshin, I. (2010). Hardware implementation of fuzzy flip-flops based on Łukasiewicz norms, 9th WSEAS International Conference on Applied Computer and Applied Computational Science, Genova, Italy, pp. 196–201.
]Search in Google Scholar
[
Łukasik, S. and Kowalski, P.A. (2014). Fully informed swarm optimization algorithms: Basic concepts, variants and experimental evaluation, Federated Conference on Computer Science and Information Systems, Warsaw, Poland, pp. 155–161.
]Search in Google Scholar
[
Ozawa, K., Hirota, K. and Koczy, L.T. (1996). Fuzzy flip-flop, in M.Y. Patyra and D.M. Mlynek (Eds), Fuzzy Logic: Implementation and Applications, Wiley/BG Teunbner Publ., pp. 197–236.10.1007/978-3-322-88955-3_7
]Search in Google Scholar
[
Ozawa, K., Hirota, K., Koczy, L.T. and Omori, K. (1991). Algebraic fuzzy flip-flop circuits, Fuzzy Sets and Systems 39(2): 215–226.10.1016/0165-0114(91)90214-B
]Search in Google Scholar
[
Pu, X., Fang, Z. and Liu, Y. (2007). Multilayer perceptron networks training using particle swarm optimization with minimum velocity constraints, in D. Liu et al. (Eds), Advances in Neural Networks, Lecture Notes in Computer Science, Vol. 493, Springer, Berlin/Heidelberg, pp. 237–245.10.1007/978-3-540-72395-0_31
]Search in Google Scholar
[
Rakitianskaia, A. and Engelbrecht, A. (2015). Saturation in PSO neural network training: Good or evil?, IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, pp. 125–132.
]Search in Google Scholar
[
Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer, Berlin/Heidelberg.
]Search in Google Scholar
[
Siminski, K. (2021). An outlier-robust neuro-fuzzy system for classification and regression, International Journal of Applied Mathematics and Computer Science 31(2): 303–319, DOI: 10.34768/amcs-2021-0021.
]Search in Google Scholar
[
Talbi, E.-G. (2009). Metaheuristics: From Design to Implementation, Wiley, Hoboken.10.1002/9780470496916
]Search in Google Scholar
[
Tsoulos, I.G., Tzallas, A. and Karvounis, E. (2021). Improving the PSO method for global optimization problems, Evolving Systems 12: 1–9, DOI: 10.1007/s12530-020-09330-9.10.1007/s12530-020-09330-9
]Search in Google Scholar
[
Zavala, A.H., Nieto, O.C., Batyrshin, I. and Vargas, L.V. (2009). VLSI implementation of a module for realization of basic t-norms on fuzzy hardware, IEEE International Conference on Fuzzy Systems, Jeju, South Korea, pp. 655–659.
]Search in Google Scholar
