Fuzzy convolutional neural network model applied to classification problems

Article type: Research Article

Authors: Gonzalez, Claudia I.^{; *} | Torres, Cesar

Affiliations: Division of Graduate Studies and Research, Tijuana Institute of Technology/TecNM, Tijuana, Mexico

Correspondence: [*] Corresponding author. Claudia I. Gonzalez, Division of Graduate Studies and Research, Tijuana Institute of Technology/TecNM, Tijuana 22414, Mexico. E-mail: cgonzalez@tectijuana.mx.

Abstract: This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.

Keywords: Fuzzy-neural network, fuzzy CNN, fuzzy deep learning model, fuzzy data, fuzzy convolutional

DOI: 10.3233/JIFS-219369

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024