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New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification
International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):336-345
Published online December 25, 2020
© 2020 Korean Institute of Intelligent Systems.

P. Murugeswari1 and S. Vijayalakshmi2

1Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore,Tamilnadu, India
2Department of Computer Applications, NMS S. Vellaichamy Nadar College, Madurai, Tamilnadu, India
Correspondence to: P. Murugeswari (pmurugeswarik7@gmail.com)
Received August 26, 2020; Revised December 8, 2020; Accepted December 15, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In the last two decades, neural networks and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network (FNN) system framework infers the union of fuzzy logic and neural network system framework thoughts, which consolidates their advantages. The FNN system is applied in several scientific and engineering areas. Wherever there is uncertainty associated with the data, fuzzy logic places a vital rule. The fuzzy set can effectively represent and handle uncertain information. The main objective of the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either the neural network structures, activation functions, or learning algorithms. In computer vision and intelligent systems, convolutional neural networks (CNNs) have more popular architectures, and their performance is excellent in many applications. In this paper, fuzzy-based CNN image classification methods are analyzed, and an interval type-2 fuzzy-based CNN is proposed. The experimental results indicated that the performance of the proposed method was good.
Keywords : CNN, FCNN, Fuzzy logic, Interval type-2 fuzzy logic, Feature extraction, Computer vision, Image classification