Enhanced L_2-L_∞ state estimation design for delayed neural networks including leakage term via quadratic-type generalized free-matrix-based integral inequality

摘要

The issue of L-2-L-infinity state estimation design for delayed neural networks (NNs) has been considered in this proposal under the leakage delay effects. By utilizing time-delay information sufficiently together with a suitable Lyapunov-Krasovskii functionals (LKFs), and estimating their derivative via quadratic generalized free-matrix-based integral inequality (QGFMBII) to access a stronger upper bound of the integral term. As a result, a new L-2-L-infinity state estimation criterion has been launched in term of linear matrix inequalities (LMIs), which includes both the time-varying delay function is differentiable and non-differentiable cases. It is amazing that the leakage delay has a disrupting effect on the state estimation performance of NNs and they cannot be avoided. At the end, this was demonstrated to facilitate the effectiveness and applicability of the designed state estimation technique using well-studied numerical examples together with a four-tank system model as a practical example in terms of the NN model. Also, the illustrative simulation results confirm that the developed state estimation technique performs well and is successful in this proposal where existing techniques fail. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.