穩(wěn)態(tài)優(yōu)化問題就是依據(jù)過程的數(shù)學(xué)模型，在約束條件下，優(yōu)化其目標(biāo)函數(shù)，而實(shí)際的工業(yè)過程往往是呈非線性或慢時(shí)變性。針對(duì)動(dòng)態(tài)非線性大工業(yè)過程, 提出了得到其可分穩(wěn)態(tài)模型強(qiáng)一致性估計(jì)的分散辨識(shí)方法；利用多項(xiàng)式對(duì)非線性函數(shù)的無限逼近的性質(zhì)和優(yōu)化過程中設(shè)定點(diǎn)的階躍信號(hào)作輸入激勵(lì)信號(hào)，獲得了動(dòng)態(tài)非線性大工業(yè)過程的可分穩(wěn)態(tài)模型和可辨識(shí)的條件。
關(guān) 鍵 詞 非線性大工業(yè)過程; 穩(wěn)態(tài)模型; 強(qiáng)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