subsequently, a scheme of modeling unknown nonlinear systems is presented . in it a feedforward structure of wavelet neural networks is adopted, and with a hybrid error performance index, a novel on-line dynamic gradient algorithm for training neural networks is put forward . as a result, the real system state and the derivative of the state are accurately identified at the same time, thus a more accurate network model is obtained 隨后考慮了對未知非線性動態(tài)系統(tǒng)進(jìn)行建模的方案,其中采用了前向的小波神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),通過選取一種混合的誤差性能指標(biāo),提出一種新的在線動態(tài)梯度算法訓(xùn)練神經(jīng)網(wǎng)絡(luò),使得最終實現(xiàn)對未知系統(tǒng)狀態(tài)及其導(dǎo)數(shù)(系統(tǒng)函數(shù))同時精確辨識,從而得到一個較為精確的網(wǎng)絡(luò)模型。