| dc.contributor.author | Lin, Hairong | |
| dc.contributor.author | Wang, Chunhua | |
| dc.contributor.author | Sun, Yichuang | |
| dc.contributor.author | Wang, Ting | |
| dc.date.accessioned | 2023-10-02T12:15:01Z | |
| dc.date.available | 2023-10-02T12:15:01Z | |
| dc.date.issued | 2022-10-06 | |
| dc.identifier.citation | Lin , H , Wang , C , Sun , Y & Wang , T 2022 , ' Generating n-Scroll Chaotic Attractors From A Memristor-based Magnetized Hopfield Neural Network ' , IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 70 , no. 1 , pp. 311 - 315 . https://doi.org/10.1109/TCSII.2022.3212394 | |
| dc.identifier.issn | 1549-7747 | |
| dc.identifier.uri | http://hdl.handle.net/2299/26811 | |
| dc.description | © 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSII.2022.3212394 | |
| dc.description.abstract | This brief presents a novel method to generate n-scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified. | en |
| dc.format.extent | 5 | |
| dc.format.extent | 3614617 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | IEEE Transactions on Circuits and Systems II: Express Briefs | |
| dc.title | Generating n-Scroll Chaotic Attractors From A Memristor-based Magnetized Hopfield Neural Network | en |
| dc.contributor.institution | Centre for Future Societies Research | |
| dc.contributor.institution | School of Physics, Engineering & Computer Science | |
| dc.contributor.institution | Department of Engineering and Technology | |
| dc.contributor.institution | Communications and Intelligent Systems | |
| dc.contributor.institution | Centre for Engineering Research | |
| dc.description.status | Peer reviewed | |
| rioxxterms.versionofrecord | 10.1109/TCSII.2022.3212394 | |
| rioxxterms.type | Journal Article/Review | |
| herts.preservation.rarelyaccessed | true | |
