A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption
This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.847385/full |
| _version_ | 1828774076092514304 |
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| author | Fei Yu Xinxin Kong Huifeng Chen Qiulin Yu Shuo Cai Yuanyuan Huang Sichun Du |
| author_facet | Fei Yu Xinxin Kong Huifeng Chen Qiulin Yu Shuo Cai Yuanyuan Huang Sichun Du |
| author_sort | Fei Yu |
| collection | DOAJ |
| description | This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of fractional memristor Hopfield neural network is studied. Because this 6D-FMHNN has chaotic characteristics, we also use this 6D-FMHNN to generate a random number and apply it to the field of image encryption. We make a series of analysis on the randomness of random numbers and the security of image encryption, and prove that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key. |
| first_indexed | 2024-12-11T15:14:42Z |
| format | Article |
| id | doaj.art-6ddb6baf550649af98f5a09e39014ed9 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-12-11T15:14:42Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-6ddb6baf550649af98f5a09e39014ed92022-12-22T01:00:37ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-03-011010.3389/fphy.2022.847385847385A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image EncryptionFei Yu0Xinxin Kong1Huifeng Chen2Qiulin Yu3Shuo Cai4Yuanyuan Huang5Sichun Du6School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaThis paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of fractional memristor Hopfield neural network is studied. Because this 6D-FMHNN has chaotic characteristics, we also use this 6D-FMHNN to generate a random number and apply it to the field of image encryption. We make a series of analysis on the randomness of random numbers and the security of image encryption, and prove that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key.https://www.frontiersin.org/articles/10.3389/fphy.2022.847385/fullchaotic systemfractional-orderHopfield neural networkmemristorbifurcationimage encryption |
| spellingShingle | Fei Yu Xinxin Kong Huifeng Chen Qiulin Yu Shuo Cai Yuanyuan Huang Sichun Du A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption Frontiers in Physics chaotic system fractional-order Hopfield neural network memristor bifurcation image encryption |
| title | A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption |
| title_full | A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption |
| title_fullStr | A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption |
| title_full_unstemmed | A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption |
| title_short | A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption |
| title_sort | 6d fractional order memristive hopfield neural network and its application in image encryption |
| topic | chaotic system fractional-order Hopfield neural network memristor bifurcation image encryption |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.847385/full |
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