A new result on H∞ performance state estimation for static neural networks with time-varying delays

Highlights：

• A parameter-dependent reciprocally convex inequality is proposed to estimate the derivative of the LKF, which encompasses some existing results as its special cases such as [29-31], in which the two parameters can be chosen freely and independently.

• Compared with the existing methods such as in [23, 28, 29, 32], this paper fully considers the free structure of the introduced slack matrices, which directly lead to the reduction of conservativeness in the estimator solution.

摘要

•By dividing the estimation error of activation function into two parts, an improved Lyapunov-Krasovskii functional (LKF) is constructed, in which the slope information of activation function (SIAF) can be fully captured compared with those in [23, 28].•A parameter-dependent reciprocally convex inequality is proposed to estimate the derivative of the LKF, which encompasses some existing results as its special cases such as [29-31], in which the two parameters can be chosen freely and independently.•Compared with the existing methods such as in [23, 28, 29, 32], this paper fully considers the free structure of the introduced slack matrices, which directly lead to the reduction of conservativeness in the estimator solution.

论文关键词：Static neural networks,Activation function,H∞ performance state estimation,Parameter-dependent reciprocally convex inequality,Decoupling principle

论文评审过程：Received 18 March 2020, Revised 23 May 2020, Accepted 19 July 2020, Available online 31 July 2020, Version of Record 31 July 2020.