Paper Info
Title
Rotary Kiln Intelligent Control Based on T-S Fuzzy Neural Network and Rough Sets
Abstract
Based on the idea of the knowledge reduction of the rough sets (RS) theory and the nonlinearity mapping of Takagi-Sugeno fuzzy neural network (FNN), a kind of RS-FNN intelligent control method is presented and applied in the rotary kiln sintering process due to its nonlinearities in the dynamics and the large dimensionality of the problem. Firstly, fuzzy c-means (FCM) clustering method based on a new cluster validity index is used to obtain the optimal discrete values of the continuous attributes. Then, RS theory is adopted to obtain the reductive rules using industrial history datum and corresponding FNN model has better topology configuration. Finally, the structure parameters of T-S fuzzy model are fine-tuned by a hybrid algorithm integrating the gradient descent method with least-squares estimation. The results of simulation as well as temperature control for an industrial rotary kiln furnace of iron ore oxidized pellets sintering process were performed to demonstrate the feasibility and effectiveness of the proposed scheme.
Year
DOI
Venue
2007
10.1109/FSKD.2007.494
FSKD (2)
Keywords
Field
DocType
rotary kiln sintering process,rough set theory,sintering,t-s fuzzy neural network,pattern clustering,temperature control,corresponding fnn model,topology configuration,industrial rotary kiln furnace,fuzzy c-means clustering,takagi-sugeno fuzzy neural network,gradient descent method,fuzzy c-means,minerals,optimal discrete values,t-s fuzzy model,estimation theory,kilns,clustering method,rs-fnn intelligent control,least-squares estimation,knowledge reduction,iron,rough sets,nonlinearity mapping,gradient methods,rs theory,iron ore oxidized pellets sintering process,fnn model,industrial history datum,rs-fnn intelligent control method,rotary kiln intelligent control,fuzzy neural nets,cluster validity index,least squares estimation,rough set,indexation,hybrid algorithm,fuzzy neural network,intelligent control
Intelligent control,Gradient descent,Computer science,Control theory,Fuzzy logic,Rotary kiln,Rough set,Cluster analysis,Artificial neural network,Kiln
Conference
Volume
ISBN
Citations 
2
978-0-7695-2874-8
0
PageRank 
References 
Authors
0.34
5
1
Name
Order
Citations
PageRank
Jie-sheng Wang133.44