A PWA model identification method for nonlinear systems using hierarchical clustering based on the gap metric

2020 ◽  
Vol 138 ◽  
pp. 106838
Author(s):  
Jiaorao Wang ◽  
Chunyue Song ◽  
Jun Zhao ◽  
Zuhua Xu
Keyword(s):  
Nonlinear Systems ◽  
Hierarchical Clustering ◽  
Model Identification ◽  
Gap Metric ◽  
Identification Method

scholarly journals T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

2020 ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jingfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

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