Further Result on H∞ Performance State Estimation of Delayed Static Neural Networks Based on an Improved Reciprocally Convex Inequality
G Tan, Z Wang - IEEE Transactions on Circuits and Systems II …, 2019 - ieeexplore.ieee.org
G Tan, Z Wang
IEEE Transactions on Circuits and Systems II: Express Briefs, 2019•ieeexplore.ieee.orgIn this brief, an improved reciprocally convex inequality is presented to analyse the problem
of H∞ performance state estimation for static neural networks. A tight upper bound of time-
derivative for the Lyapunov functional is handled by the improved reciprocally convex
inequality. Then, a less conservative H∞ performance state estimation criterion is derived.
As a result, the criterion is employed to present a method for designing suitable estimator
gain matrices. A numerical example is used to illustrate the effectiveness of the proposed …
of H∞ performance state estimation for static neural networks. A tight upper bound of time-
derivative for the Lyapunov functional is handled by the improved reciprocally convex
inequality. Then, a less conservative H∞ performance state estimation criterion is derived.
As a result, the criterion is employed to present a method for designing suitable estimator
gain matrices. A numerical example is used to illustrate the effectiveness of the proposed …
In this brief, an improved reciprocally convex inequality is presented to analyse the problem of H ∞ performance state estimation for static neural networks. A tight upper bound of time-derivative for the Lyapunov functional is handled by the improved reciprocally convex inequality. Then, a less conservative H ∞ performance state estimation criterion is derived. As a result, the criterion is employed to present a method for designing suitable estimator gain matrices. A numerical example is used to illustrate the effectiveness of the proposed method.
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