对参数不确定神经网络的鲁棒状态估计

讨论了参数不确定神经网络的状态估计问题.由于参数的不确定性，无法设计观测器使观测器的状态与原来的神经网络达到完全同步,只能将误差控制在一定范围之内.对于给定的观测器中的增益矩阵，给出了判据来估计观测器的状态与原神经网络的状态间最终的误差界；同时，通过使用线性矩阵不等式的技术，给出了观测器中增益矩阵的设计方法.此外，对误差界的估计进行讨论，说明了影响估计准确性的主要原因.最后用2个例子来说明这些判据的有效性.

Robust State Estimation for Uncertain Neural Networks

The robust state estimation problem for uncertain neural network is studied in this paper.As the uncertainty of the parameters,the states of the estimator can't be complete synchronous with the neural network,but asymptotically synchronous with errorbound is accessible.For given state estimator gain matrix,the error bound is derived.By using stable theory and linear matrix inequality approach,the design of the robust state estimator is also given in this paper.And the discussion of the estimate of the error bound is also presented.The simulation samples have proved the effectiveness of the conclusion.