H ∞ and l 2 - l ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol

• Published in: Neural networks Vol. 132; pp. 121 - 130
• Main Authors: Liu, Hongjian, Wang, Zidong, Fei, Weiyin, Li, Jiahui
• Format: Journal Article
• Language: English
• Published: United States 01.12.2020
• Subjects: Neural Networks, Computer; Time Factors; Round-Robin protocol; Memristive neural networks; and - performance; State estimation; Finite-horizon
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2020.08.006

In this paper, a protocol-based finite-horizon H and l -l estimation approach is put forward to solve the state estimation problem for discrete-time memristive neural networks (MNNs) subject to time-varying delays and energy-bounded disturbances. The Round-Robin protocol is utilized to mitigate unnecessary network congestion occurring in the sensor-to-estimator communication channel. For the delayed MNNs, our aim is to devise an estimator that not only ensures a prescribed disturbance attenuation level over a finite time-horizon, but also keeps the peak value of the estimation error within a given range. By resorting to the Lyapunov-Krasovskii functional method, the delay-dependent criteria are formulated that guarantee the existence of the desired estimator. Subsequently, the estimator gains are obtained via figuring out a bank of convex optimization problems. The validity of our estimator is finally shown via a numerical example.