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 ne...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.展开更多
Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the di...Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the difference between the barrier coverage and blanket coverage. In this paper, we propose two algorithms, namely, local tree based no-way and back (LTNWB) algorithm and sensor minimum cut sets (SMCS) algorithm, for the opened and closed belt regions to determine the degree of the barrier coverage of WSN. Our main objective is to minimize the complexity of these algorithms. For the opened belt region, both algorithms work well, and for the closed belt region, they will still come into existence while some restricted conditions are taken into consideration. Finally, the simulation results demonstrate the feasibility of the proposed algorithms.展开更多
文摘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.
文摘Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the difference between the barrier coverage and blanket coverage. In this paper, we propose two algorithms, namely, local tree based no-way and back (LTNWB) algorithm and sensor minimum cut sets (SMCS) algorithm, for the opened and closed belt regions to determine the degree of the barrier coverage of WSN. Our main objective is to minimize the complexity of these algorithms. For the opened belt region, both algorithms work well, and for the closed belt region, they will still come into existence while some restricted conditions are taken into consideration. Finally, the simulation results demonstrate the feasibility of the proposed algorithms.
