Block Sparse Representations in Modified Fuzzy C-Regression Model Clustering Algorithm for TS Fuzzy Model Identification

2015 ◽  
Author(s):  
Tanmoy Dam ◽  
Alok Deb
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model ◽  
Sparse Representations ◽  
Ts Fuzzy Model

T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

2009 ◽  
Vol 22 (4-5) ◽  
pp. 646-653 ◽  
Author(s):  
Chaoshun Li ◽  
Jianzhong Zhou ◽  
Xiuqiao Xiang ◽  
Qingqing Li ◽  
Xueli An
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model

Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model

2020 ◽  
Vol 19 (04) ◽  
pp. 2050029
Author(s):  
Jianzhong Shi
Keyword(s):  
Regression Model ◽  
Fluidized Bed ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Circulating Fluidized Bed ◽  
Fuzzy Model ◽  
Identification Algorithm ◽  
Identification Process ◽  
Bed Temperature ◽  
Hyper Plane

Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership function of T-S fuzzy model. However, Gaussian fuzzy membership function was more suitable for clustering algorithm using point to point distance, such as fuzzy c-means (FCM). In this paper, a hyper-plane-shaped FCRM clustering algorithm for T-S fuzzy model identification algorithm is proposed. The antecedent membership function of proposed identification algorithm is defined by a hyper-plane-shaped membership function and an improved fuzzy partition method is applied. To illustrate the efficiency of the proposed identification algorithm, the algorithm is applied in four nonlinear systems which shows higher identification accuracy and simplified identification process. At last, the algorithm is used in a circulating fluidized bed boiler bed temperature identification process, and gets better identification result.

Water Level Control System of Boiler Drum Based on GPC

2011 ◽  
Vol 467-469 ◽  
pp. 984-989
Author(s):  
Er Fei Dou ◽  
Hong Xing Li
Keyword(s):  
Control System ◽  
Model Identification ◽  
Fuzzy Model ◽  
Level Control ◽  
Control Effect ◽  
Boiler Plant ◽  
Ts Fuzzy Model ◽  
Level Control System ◽  
Clustering Approach ◽  
Boiler Drum

The paper discusses a TS fuzzy model identification method for an industrial drum-type boiler plant using the fuzzy clustering approach. The fuzzy model is constructed from a set of input-output data that covers a wide operating range of the plant. Then this paper studies the application of GPC algorithm in drum level control system Based on the T-S fuzzy model, simulates the control effect of the controller in the MATLAB environment, and analyze the performance of the control system.

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