Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy （NF） modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy （RLLNF） network trained by local linear model tree （LOLIMOT）, which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron （MLP） and radial basis function （RBF）. Comparison of our proposed non-linear models and associated models obtained through the least square error （LSE） technique （the optimal modelling method for linear systems） confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.