A T-S Fuzzy Neural Network Based on Rough Sets and Its Application to Rotary Kiln Sintering Process

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 control approach is presented and applied in the rotary kiln sintering process due to its nonlinearities in the dynamics and the large dimensionality of the problem.First,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 proposed(approach) was applied to control rotary kiln iron ore oxidized pellets sintering process and good results were(obtained.)