The results obtained in this paper are very important for the further studies on the state estimation and optimal control of 2-D linear systems with stochastic input.
这些结果对进一步展开对随机2-D系统的状态估计，最优控制和其它方面的研究都具有重要意义。
The learning process of a neural network is considered the process in which neural network variables are changed with a time series of input signals generated from a stochastic information source.
根据该理论，学习过程就是由随机信息源产生的输入信号驱动神经网络参数不断修改的过程；
The relations between spectrum densities of system output responses and frequency were obtained by assuming input functions to be ergodic and stable stochastic processes.
假设输入函数为各态历经的平稳随机过程，得出各输出响应的谱密度与频率的关系。
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