Fuzzy Model Identification With Enhanced Validity Criterion for Mechanical System Design

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Kuo-Ho Su
Keyword(s):  
System Design ◽  
Mechanical System ◽  
Clustering Algorithm ◽  
Structural Information ◽  
Model Identification ◽  
Fuzzy Model ◽  
Optimal Number ◽  
Identification Algorithm ◽  
Mathematical Tool ◽  
Validity Criterion

Model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzy model identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input–output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi–Sugeno fuzzy model is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzy model identification requires fewer fuzzy rules and a shorter tuning time.

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.

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