Abstract
A non-fragile memory feedback control methodology is precisely proposed in this study for synchronization of hierarchical hybrid coupled neural networks (HHCNNs) over finite-time domain with mismatched quantization channels and external disturbances. Specifically, the considered network model incorporates both higher level deterministic switching and lower level Markov switching. Moreover, an undirected communication topology is selected to project the addressed HHCNNs. The foremost intention of this study is to substantiate the synchronization criterion over finite interval of time with proposed \(H_{\infty }\) disturbance attenuation. In consideration to this motive, by conferring Lyapunov stability theory in conjunction with average dwell-time technique, a collection of adequate conditions is established for assuring the exponential synchronization criterion through a set of linear matrix inequalities. Moreover, the desired the memory feedback controller with gain variations is computed based on the developed matrix inequalities. Finally, the developed theoretical results are validated through a numerical example, which showcases the significance and advantage of the developed control strategy.
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Appendix I
Appendix I
Taking advantage of infinitesimal operator [18] indicated here as \({\mathcal {L}}\) on (18), we attained the following equations:
Now, by the agency of Jensen’s inequality [16], the integral terms in (31) can be simplified as follows:
According to \(\pi _{qn}\ge 0 (q\ne n),\ Q_{n}\ge 0\) and (13), we have
Following the same methodology as above, we can get
Moreover, by utilizing Assumption 1, we can retrieve the following inequalities:
where \(\varSigma _1\) and \(\varSigma _{2,p,q}\) are formerly described in theorem statement.
At the same instant, for any non-singular pertinent matrix \(W_{p,q}\), the following equation holds:
Further, from (28)-(39) with addition of \(z^{T}(s)z(s)-\gamma ^2 w^{T}(s)w(s)\) and taking mathematical expectations, we can easily fetch that
Moreover, from relations (12) and (40), it is easy to obtain
where \(\varTheta (t,p,q)=\bigg [\varPsi ^{T}(t) \ \ \varPsi ^{T}(t-\tau _{p,q}(t)) \ \ \varPsi ^{T}(t-\tau ^1_{p,q}(t)) \ \varPsi ^{T}(t-\tau ^2_{p,q}(t)) \ \ g^{T}(\varPsi (t)) \ \ g^{T}(\varPsi (t-\tau _{p,q}))\ \ {\dot{\varPsi }}^{T}(t)\ \ w^{T}(s) \bigg ]^\mathrm{T}.\)
Further, multiplying \(e^{-\alpha t}\) and integrating (41), we get
Moreover, in light of (15)-(17) and (40), we acquire
Based on the relations (42) and (43), and setting the recursion from \(t_{k-1}\) to \(t_k\), \(t_{k-2}\) to \(t_{k-1}\), \(\ldots \) up to \(t_0\), we bring off inequality by
Then, by letting \({\tilde{P}}_{p,q}={\mathfrak {D}}_{p,q}^{-\frac{1}{2}}P_{p,q}{\mathfrak {D}}_{p,q}^{-\frac{1}{2}}, {\tilde{Q}}_{1,p,q}={\mathfrak {D}}_{p,q}^{-\frac{1}{2}}Q_{1,p,q}{\mathfrak {D}}_{p,q}^{-\frac{1}{2}},\quad {\tilde{R}}_{1,p,q}={\mathfrak {D}}_{p,q}^{-\frac{1}{2}}R_{1,p,q}{\mathfrak {D}}_{p,q}^{-\frac{1}{2}}, {\tilde{S}}_{1,p,q}={\mathfrak {D}}_{p,q}^{-\frac{1}{2}}S_{1,p,q}{\mathfrak {D}}_{p,q}^{-\frac{1}{2}},\quad {\tilde{Q}}={\mathfrak {D}}^{-\frac{1}{2}}Q{\mathfrak {D}}^{-\frac{1}{2}}, {\tilde{R}}={\mathfrak {D}}^{-\frac{1}{2}}R{\mathfrak {D}}^{-\frac{1}{2}},\quad {\tilde{S}}={\mathfrak {D}}^{-\frac{1}{2}}S{\mathfrak {D}}^{-\frac{1}{2}},\ \tilde{E_1}={\mathfrak {D}}^{-\frac{1}{2}}E_1{\mathfrak {D}}^{-\frac{1}{2}}, \tilde{E_2}={\mathfrak {D}}^{-\frac{1}{2}}E_2{\mathfrak {D}}^{-\frac{1}{2}}\) and it is easy to obtain
Furthermore from (18), it follows that
From (44), we can get
Then, in accordance with (46) and (48), we have
From the relation (14), it is obvious that \({\mathbb {E}}\{\varPsi (t){\mathfrak {D}}_{p,q}\varPsi (t)\}<C_2 \ \forall t \in [0,{z}]\). Hence, finite-time exponential synchronization of HHCNNs (1) is achieved in accordance with Definition 2.1 of [30] under the control design (5). Thus, the proof of this theorem ends.
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Sakthivel, R., Aravinth, N., Aouiti, C. et al. Finite-time synchronization of hierarchical hybrid coupled neural networks with mismatched quantization. Neural Comput & Applic 33, 16881–16897 (2021). https://doi.org/10.1007/s00521-021-06049-9
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DOI: https://doi.org/10.1007/s00521-021-06049-9