在随机线性不确定系统中，设计一个满意的状态估计器在工程中有很大的实际意义。
In the Linear stochastic systems with uncertainties, designing a satisfactory State-estimation is of practical significance in the field of engineering.
讨论的问题包括线性调节器，状态估计器，状态线性函数观测器，及不完全或有噪声测量的线性二次控制。
The discussion includes linear regulator, state estimator, observer for a linear functional of state, and linear quadratic control with incomplete or noisy measurements.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
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