Conference Proceeding

Improved T-S fuzzy model identification approach and its application in power plants

Dept. of Autom., North China Electr. Power Univ., Beijing, China
08/2010; pp.53 - 58 In proceeding of: Modelling, Identification and Control (ICMIC), The 2010 International Conference on
Source: IEEE Xplore

ABSTRACT Systems in power plants often contain nonlinearity, complexity and randomicity. It is difficult to build their model by traditional methods. An improved fuzzy identification approach based on Takagi-Sugeno (T-S)model is proposed to solve the problem. In this paper, T-S model is firstly modified to make its identification easier. Following that, input vector is determined by heuristic knowledge and exponential form membership function is used to avoid conclusion can not be calculated. Then, entropy cluster algorithm is analyzed and improved to automatically determine the number of subspace and initial subspace centers. Finally, competitive learning algorithm and weighted recursive least-square algorithm are used to estimate the parameters of T-S model. Simulation results show that the proposed approach can describe nonlinear system in power plants accurately, and the relevant algorithm is simple and fast.

0 0
 · 
0 Bookmarks
 · 
15 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

competitive
 
exponential form membership function
 
fast
 
improved fuzzy identification approach
 
initial subspace centers
 
input vector
 
power plants
 
proposed approach
 
relevant algorithm
 
Systems
 
T-S model
 
T-S)model
 
traditional methods
 
weighted recursive least-square algorithm
 

Guolian Hou