針對實驗室不像飛機上可以通過復(fù)雜的機械傳動裝置確定發(fā)動機轉(zhuǎn)速控制系統(tǒng)的給定轉(zhuǎn)速，在研究分析了某渦扇發(fā)動機給定轉(zhuǎn)速影響因素的基礎(chǔ)上，提出了一種確定該型發(fā)動機給定轉(zhuǎn)速的徑向基函數(shù)（RBF）神經(jīng)網(wǎng)絡(luò)算法，并以試驗數(shù)據(jù)為訓(xùn)練樣本，建立了確定發(fā)動機給定轉(zhuǎn)速的RBF 神經(jīng)網(wǎng)絡(luò)模型。通過試驗驗證，結(jié)果表明該方法具有學(xué)習(xí)速度快、計算精度高等特點，將其應(yīng)用于確定航空發(fā)動機的給定轉(zhuǎn)速是行之有效的。
關(guān)鍵詞：RBF 神經(jīng)網(wǎng)絡(luò)，航空發(fā)動機，給定轉(zhuǎn)速，燃油泵調(diào)節(jié)器
Abstract: Because aero-engine theoretic speed can be known not through setting up mechanical gearing, after analyzing the factors of affecting some turbofan engine theoretic speed , we present a RBF network arithmetic that can makes certain of aero-engine theoretic speed. We set up the RBF network model that can make certain of aero-engine theoretic speed by using experimental data as the network’s learning stylebook. Through experiment,
the result shows that the method has the advantage of faster learning rate, higher calculating precision, that it can be used to make certain of aero-engine theoretic speed.
Keywords: RBF NN, aero-engine, theoretic speed, oil-pump adjustor.