Hidden Multistability in a Memristor-Based Cellular Neural Network
In this paper, we report a novel memristor-based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor-based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2020/9708649 |
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| _version_ | 1849693869883523072 |
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| author | Birong Xu Hairong Lin Guangyi Wang |
| author_facet | Birong Xu Hairong Lin Guangyi Wang |
| author_sort | Birong Xu |
| collection | DOAJ |
| description | In this paper, we report a novel memristor-based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor-based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coexisting attractors, and initial boosting behaviors of position and amplitude. Furthermore, both heterogeneous multistability and homogenous multistability are found in the CNN. Finally, Multisim circuit simulations are performed to prove the chaotic characteristics and multistability of the system. |
| format | Article |
| id | doaj-art-4f8f185934c84b1bbc375f66afca1798 |
| institution | DOAJ |
| issn | 1687-9120 1687-9139 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-4f8f185934c84b1bbc375f66afca17982025-08-20T03:20:16ZengWileyAdvances in Mathematical Physics1687-91201687-91392020-01-01202010.1155/2020/97086499708649Hidden Multistability in a Memristor-Based Cellular Neural NetworkBirong Xu0Hairong Lin1Guangyi Wang2College of Mechanic and Electronic Engineering, Wuyi University, Wuyishan 354300, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaInstitute of Modern Circuits and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn this paper, we report a novel memristor-based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor-based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coexisting attractors, and initial boosting behaviors of position and amplitude. Furthermore, both heterogeneous multistability and homogenous multistability are found in the CNN. Finally, Multisim circuit simulations are performed to prove the chaotic characteristics and multistability of the system.http://dx.doi.org/10.1155/2020/9708649 |
| spellingShingle | Birong Xu Hairong Lin Guangyi Wang Hidden Multistability in a Memristor-Based Cellular Neural Network Advances in Mathematical Physics |
| title | Hidden Multistability in a Memristor-Based Cellular Neural Network |
| title_full | Hidden Multistability in a Memristor-Based Cellular Neural Network |
| title_fullStr | Hidden Multistability in a Memristor-Based Cellular Neural Network |
| title_full_unstemmed | Hidden Multistability in a Memristor-Based Cellular Neural Network |
| title_short | Hidden Multistability in a Memristor-Based Cellular Neural Network |
| title_sort | hidden multistability in a memristor based cellular neural network |
| url | http://dx.doi.org/10.1155/2020/9708649 |
| work_keys_str_mv | AT birongxu hiddenmultistabilityinamemristorbasedcellularneuralnetwork AT haironglin hiddenmultistabilityinamemristorbasedcellularneuralnetwork AT guangyiwang hiddenmultistabilityinamemristorbasedcellularneuralnetwork |