A Fault Detection Method of Memristor in Chaotic Circuit Based on Artificial Neural Network. (27th July 2022)

• Record Type: Journal Article
• Authors: Huang, Shishi; Ma, Tianyu
• Other Names: Lakshmanna Kuruva Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-27
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/5429513
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Memristor is the fourth basic circuit component after the three basic circuit components of resistance, capacitance and inductance. It has the characteristics of nonlinear characteristics and memory function and shows great application prospects in the fields of memory, neural network, logic operation, chaotic circuit and so on. The appearance of memristor provides a new choice for the circuit realization of chaotic system. The chaotic circuit based on memristor has different characteristics compared with the chaotic circuit constructed by general components. Therefore, we studied the chaotic circuit based on memristor. It will be of great significance to the application of memristive chaotic attractors in practical engineering. In order to improve the accuracy and effectiveness of memristors in various circuits, the method of combining the artificial neural network and fuzzy analysis is used for fault detection of memristors in chaotic circuits, which can accurately judge whether the memristors in the circuit are faulty. The work of this paper is as follows: (1) development status of memristors and the detection methods related to memristor faults are introduced in detail. This paper also introduces and summarizes the development of machine learning, especially neural networks considering references of related theories in memristor fault detection. (2) Introduced the relevant theory of CNN and proposed to use one-dimensional CNN for fault diagnosis. Based on this, an improved method, namely, artificial feature enhancement model, was proposed. (3) The memristor fault detection data set was designed, and the validity of the structure and parameters of the selected CNN is verified. Finally, the effectiveness and superiority of the improvement are verified by the comparison of the CNN model and the artificial feature enhancement model.