Name
Title | ||
|---|---|---|
Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1631/jzus.C11a0278 | Journal of Zhejiang University: Science C |
Keywords | Field | DocType |
non-linear system identification,local linear model tree (lolimot),recurrent local linear neuro-fuzzy (rllnf) network,neural network (nn),industrial winding process | Neuro-fuzzy,Radial basis function,Nonlinear system,Identifier,Linear system,Computer science,Linear model,Control theory,Multilayer perceptron,Least square error | Journal |
Volume | Issue | ISSN |
13 | 6 | 1869-196X |
Citations | PageRank | References |
3 | 0.42 | 3 |
Authors | ||
11 | ||