具有泄漏时滞和混合加性时变时滞复数神经网络的状态估计

研究了具有泄漏时滞、加性离散时变时滞、加性分布时变时滞复数神经网络的状态估计问题.在复数神经网络不分解条件下, 通过构造合适的Lyapunov-Krasovskii泛函, 并应用自由权矩阵、矩阵不等式和倒数凸组合法等方法, 通过可观测的输出测量来估计神经元状态, 给出了判断误差状态模型全局渐近稳定的与时滞相关的复数线性矩阵不等式.最后, 通过一个数值仿真算例验证了理论分析的有效性.

State Estimation of Complex-Valued Neural Networks With Leakage Delay and Mixed Additive Time-Varying Delays

The state estimation of complex-valued neural networks with leakage delay and both discrete and distributed additive time-varying delays was studied. In the case where the activation function of the network was not required to be separated, through construction of the appropriate Lyapunov-Krasovskii functionals, and with the free weight matrix, the matrix inequality and the reciprocal convex combination method, the state of the neuron was estimated by means of observable output measurements. In addition, complex-valued linear matrix inequalities related to time delays were given to ensure the global asymptotic stability of the error-state model. Finally, numerical simulation examples verify the validity of the theoretical analysis.