Prediction Using a Fuzzy Inference System in the Classification Layer of a Convolutional Neural Network Replacing the Softmax Function

1. Weller, A., Bischof, G.N., Schlueter, P., Richter, N., Dronse, J., Onur, O., Neumaier, B., Kukolja, J., Langen, K.J., Fink, G., unoth, A.: Finding New Communities: A Principle of Neuronal Network Reorganization in Alzheimer’s Disease. Brain Connect, 11(3), 225–238 (2021). https://doi.org/10.1089/brain.2020.0889.

2. Miramontes, I., Melin, P.: Interval Type-2 Fuzzy Approach for Dynamic Parameter Adaptation in the Bird Swarm Algorithm for the Optimization of Fuzzy Medical Classifier. Axioms 11(9), 485 (2022). https://doi.org/10.3390/axioms11090485

   Article  Google Scholar 

3. Dichgans, J., Kallwies, J., Wuensche, H.-J.: Robust Vehicle Tracking with Monocular Vision using Convolutional Neuronal Networks. In: 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), IEEE, pp. 297–302 (2020). https://doi.org/10.1109/MFI49285.2020.9235213.

4. Varela-Santos, S., Melin, P.: A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images. Expert Syst. Appl. 168, 114361 (2021). https://doi.org/10.1016/j.eswa.2020.114361

   Article  Google Scholar 

5. Naim, S., Chaibi, H., Abdessamad, E.R., Saadane, R., Chehri, A.: A Hybrid Automatic Facial Expression Recognition Based on Convolutional Neuronal Networks and Support Vector Machines Techniques. pp. 27–39 (2022). https://doi.org/10.1007/978-981-19-3455-1_3

6. Varela-Santos, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf Sci (N Y) 545, 403–414 (2021). https://doi.org/10.1016/j.ins.2020.09.041

   Article  MathSciNet  Google Scholar 

7. S. Mehra, G. Raut, R. Das Purkayastha, S. K. Vishvakarma, and A. Biasizzo, “An Empirical Evaluation of Enhanced Performance Softmax Function in Deep Learning,” IEEE Access, vol. 11, pp. 34912–34924, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3265327.

8. Shoba, V.B.T., Sam, I.S.: Adaptive deep feature learning based Softmax regressive classification for aging facial recognition. Multimed Tools Appl 82(15), 22343–22371 (2023). https://doi.org/10.1007/s11042-022-14129-8

   Article  Google Scholar 

9. Zhang, Y., Peng, L., Quan, L., Zhang, Y., Zheng, S., Chen, H.: High-Precision Method and Architecture for Base-2 Softmax Function in DNN Training. IEEE Trans. Circuits Syst. I Regul. Pap.Regul. Pap. 70(8), 3268–3279 (2023). https://doi.org/10.1109/TCSI.2023.3277247

   Article  Google Scholar 

10. Lee, J., Wang, Y., Cho, S.: Angular Margin-Mining Softmax Loss for Face Recognition. IEEE Access 10, 43071–43080 (2022). https://doi.org/10.1109/ACCESS.2022.3168310

    Article  Google Scholar 

11. Asghar, S., et al.: Water Classification Using Convolutional Neural Network. IEEE Access 11, 78601–78612 (2023). https://doi.org/10.1109/ACCESS.2023.3298061

    Article  Google Scholar 

12. Kodipalli, A., Devi, S.V., Dasar, S., Ismail, T.: A novel variant of deep convolutional neural network for classification of ovarian tumors using CT images. Comput. Electr. Eng.. Electr. Eng. 109, 108758 (2023). https://doi.org/10.1016/j.compeleceng.2023.108758

    Article  Google Scholar 

13. A. T. Kabakus, A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security. Concurr. Comput. Pract. Exper. 35 (2023). https://doi.org/10.1002/cpe.7517

14. Elhani, D., Megherbi, A.C., Zitouni, A., Dornaika, F., Sbaa, S., Taleb-Ahmed, A.: Optimizing convolutional neural networks architecture using a modified particle swarm optimization for image classification. Expert Syst. Appl. 229, 120411 (2023). https://doi.org/10.1016/j.eswa.2023.120411

    Article  Google Scholar 

15. Lin, C.-J., Yang, T.-Y.: A Fusion-Based Convolutional Fuzzy Neural Network for Lung Cancer Classification. Int. J. Fuzzy Syst. 25(2), 451–467 (2023). https://doi.org/10.1007/s40815-022-01399-5

    Article  MathSciNet  Google Scholar 

16. Büyükarıkan, B., Ülker, E.: Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approach. Multimed Syst 29(3), 1651–1661 (2023). https://doi.org/10.1007/s00530-023-01084-z

    Article  Google Scholar 

17. Kumar, A., Patel, V.K.: Classification and identification of disease in potato leaf using hierarchical based deep learning convolutional neural network. Multimed Tools Appl 82(20), 31101–31127 (2023). https://doi.org/10.1007/s11042-023-14663-z

    Article  Google Scholar 

18. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L.: Handwritten digit recognition with a back-propagation network. Neural networks, current applications Chapman Hall/CRC Publishers (1992)

    Google Scholar 

19. LeCun, Y. B. Y.: Convolution Networks for Images, Speech, and Time-Series. Igarss 2014, vol. 1, pp. 1–5 (1998)

    Google Scholar 

20. Maida, A.S.: Cognitive Computing and Neural Networks. pp. 39–78 (2016). https://doi.org/10.1016/bs.host.2016.07.011

21. Castro, J.R., Castillo, O., Melin, P., Rodríguez-Díaz, A.: Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox, Transactions on computational science I, 104–114. Lecture Notes in Computer Science, vol 4750. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79299-4_5

22. Melin, P., Castillo, O.: A new method for adaptive control of non-linear plants using type-2 fuzzy logic and neural networks. Int. J. Gen. Syst. 33(2–3), 289–304 (2004)

    Article  Google Scholar 

23. Castillo, O., Melin, P.: A new fuzzy-fractal-genetic method for automated mathematical modelling and simulation of robotic dynamic systems, 1998 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 1998) Proceedings. Volume 2, 1182–1187 (1998)

    Google Scholar 

24. Castillo, O., Melin, P.: Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach. Appl. Soft Comput.Comput. 3(4), 363–378 (2003)

    Article  Google Scholar 

25. Tai, K., El-Sayed, A.-R., Biglarbegian, M., Gonzalez, C.I., Castillo, O., Mahmud, S.: Review of Recent Type-2 Fuzzy Controller Applications, Algorithms, 9(2). 39 (2016)

    Google Scholar 

26. Valdez, F., Melin, P., Castillo, O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: IEEE International Conference on Fuzzy Systems, pp. 2114–2119 (2009)

    Google Scholar 

27. Valdez, F., Vazquez, J.C., Melin, P., Castillo, O.: Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl. Soft Comput. Comput. 52, 1070–1083 (2017)

    Article  Google Scholar 

28. Sanchez, D., Melin, P., Castillo, O.: A grey wolf optimizer for modular granular neural networks for human recognition. Computational intelligence and neuroscience (2017). https://doi.org/10.1155/2017/4180510

29. Melin, P., Urias, J., Solano, D., Soto, M., Lopez, M., Castillo, O.: Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms. Eng. Lett. 13(2), 108–116 (2006)

    Google Scholar 

Download references