Exponential H<sub>∞</sub> State Estimation for Memristive Neural Networks: Vector Optimization Approach.
- DOI
- 10.1109/tnnls.2020.3026707
- Published
- 2021-10-27
- Container
- IEEE Trans Neural Netw Learn Syst
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2020.3026707,
title = {Exponential H<sub>∞</sub> State Estimation for Memristive Neural Networks: Vector Optimization Approach.},
author = {Li R and Cao J.},
year = {2021},
journal = {IEEE Trans Neural Netw Learn Syst},
doi = {10.1109/tnnls.2020.3026707},
url = {https://doi.org/10.1109/tnnls.2020.3026707}
}RIS
TY - JOUR TI - Exponential H<sub>∞</sub> State Estimation for Memristive Neural Networks: Vector Optimization Approach. AU - Li R AU - Cao J. PY - 2021 JO - IEEE Trans Neural Netw Learn Syst DO - 10.1109/tnnls.2020.3026707 UR - https://doi.org/10.1109/tnnls.2020.3026707 ER -
APA
R, L., & J., C. (2021). Exponential H<sub>∞</sub> State Estimation for Memristive Neural Networks: Vector Optimization Approach.. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2020.3026707
Source records
- europe-pmc · retrieved 2026-09-25T06:10:44.307Z