A novel method named Fuzzy Least Squares method of System Identification is proposed for processing the data got from measurements with identical precision.
提出了一种新的系统辨识的方法——模糊最小二乘系统辨识法。
As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extend, to the well known statistical regression analysis.
作为主要估计方法之一的模糊最小二乘估计以其与统计最小二乘估计的密切联系更受到人们的重视。
T S fuzzy model is constructed for nonlinear system in this paper, and orthogonal least squares (OLS) method is used to identify the parameters of fuzzy ruler consequents.
对非线性系统建立T-S模糊模型，并用正交最小二乘法（OLS）对模糊规则的后件参数进行辨识。
The T-S fuzzy model's parameters are identified by methods of fuzzy C mean(FCM) and orthogonal least-squares(OLS) online or otherwise.
模糊模型的前件和后件参数分别采用模糊C均值聚类（FCM）和正交最小二乘法（OLS）进行离线或在线辨识。
A modified fuzzy partial least squares (PLS) model is presented for the characteristics of petrochemical process.
针对石油化工过程的特点，提出一种改进的模糊部分最小二乘的建模算法。
Secondly, the consequent parameters of rules are calculated by the least squares estimate. Thus, the T-S fuzzy model is set up.
然后，采用最小二乘法求得T-S模糊模型的规则后件参数，从而建立起非线性系统的T-S模糊模型。
Secondly, the consequent parameters of rules are calculated by the least squares estimate. Thus, the T-S fuzzy model is set up.
然后，采用最小二乘法求得T-S模糊模型的规则后件参数，从而建立起非线性系统的T-S模糊模型。
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