Orthogonal least squares method and its application in analytical chemistry 正交最小二乘法及其在分析化學(xué)中的應(yīng)用
The value of uncertain disturbance is studied by rbf neural network with the regulation orthogonal least square general algorithm 最后采用改進(jìn)的規(guī)范正交最小二乘算法的rbf網(wǎng)絡(luò)來學(xué)習(xí)不確定干擾因素的上界值。
Secondly , this paper presents a new fuzzy modeling based on t - s fuzzy model , which calculates the membership grade of each fuzzy subspace using fuzzy partition of input space , the consequence parameter identification is obtained by orthogonal least square 其次,提出了一種新的基于t - s模糊模型的建模方法。該方法是基于輸入空間的模糊劃分計(jì)算給定樣本在各模糊子空間的隸屬度,利用正交最小二乘算法辨識模糊模型的結(jié)論參數(shù)。
Orthogonal least squares ( ols ) method is introduced to model the strength of concrete . compared with the popular bp network , the simulation shows that the learning speed of rbf network is substantially faster than that encountered in bp network and the generalization ability of this network is also better 本文中該網(wǎng)絡(luò)采用正交學(xué)習(xí)算法( orthogonalleastsquare , ols )辨識混凝土強(qiáng)度模型,并將仿真結(jié)果與現(xiàn)在廣受歡迎的bp網(wǎng)絡(luò)比較。