2 ) by analyzing the information and conditional information description mechanism of system states , the problem of stochastic model reduction is investigated based on state aggregation . the information loss and conditional information loss between the full - and reduced - order models are measured by entropy , while the independence and conditional independence within me components of aggregated state are measured by kullback - leibler information distance . several model reduction methods for stable and unstable linear systems are derived by employing two criteria to get aggregation matrices : the minimal information loss and the maximal independence 2 )分析了隨機(jī)系統(tǒng)狀態(tài)空間模型中的信息和條件信息描述機(jī)制,以shannon熵為手段描述線性系統(tǒng)模型降階過程中的信息和條件信息損失,以kullback - leibler信息作為衡量降階模型狀態(tài)向量各分量之間統(tǒng)計(jì)獨(dú)立性的測度,針對穩(wěn)定和不穩(wěn)定系統(tǒng)研究基于狀態(tài)集聚的模型降階問題:分別運(yùn)用最小信息損失準(zhǔn)則和最大獨(dú)立性原則,得出幾種狀態(tài)集聚的信息論方法,并討論降階模型的性質(zhì)、階次的確定、系統(tǒng)噪聲分布特性等問題。