穩態優化問題就是依據過程的數學模型，在約束條件下，優化其目標函數，而實際的工業過程往往是呈非線性或慢時變性。針對動態非線性大工業過程, 提出了得到其可分穩態模型強一致性估計的分散辨識方法；利用多項式對非線性函數的無限逼近的性質和優化過程中設定點的階躍信號作輸入激勵信號，獲得了動態非線性大工業過程的可分穩態模型和可辨識的條件。
關 鍵 詞 非線性大工業過程; 穩態模型; 強一致性; 子過程

Abstract The steady-state optimization problem is optimizing objective functions based on mathematical model under constrained conditions, in fact the large-scale industrial processes are often nonlinear and slowly time varying. In allusion to dynamic nonlinear large-scale industrial processes, to bring up gained the method of decentralized identification for the strong consistency estimates of the divisible steady-state models, it is used that property of polynomial can infinitely approach to the nonlinear function and in optimization processes use step signals as input signals, the divisible steady-state models of dynamic nonlinear large-scale industrial processes, and the cognizable conditions are obtained.
Key words nonlinear large-scale industrial processes; steady-state model; strong consistency; sub processes