为了解决在采用局部线性模型树(LOcal Linear MOdel Tree,LOLIMOT)辨识发动机非线性系统时,出现的辨识网络复杂和模型精度问题,提出一种将非线性自回归滑动平均模型(NARMAX)和LOLIMOT网络融合的改进神经网络结构.基于非线性自回归滑动...为了解决在采用局部线性模型树(LOcal Linear MOdel Tree,LOLIMOT)辨识发动机非线性系统时,出现的辨识网络复杂和模型精度问题,提出一种将非线性自回归滑动平均模型(NARMAX)和LOLIMOT网络融合的改进神经网络结构.基于非线性自回归滑动平均模型NARMAX的思想,将原始局部子模型的线性函数替换为非线性多项式函数,并基于AIC(Akaike information criterion)显著性准则的前向选择法对非线性项按照重要性程度进行选择,将简化后的非线性函数用于构建原始LOLIMOT模型局部子模型,形成一种改进LOLIMOT网络模型.通过某航空发动机过渡态下的辨识实验表明,改进算法能够将原LOLIMOT模型复杂度降低46%左右,相对预测精度提高50%以上,验证了在对发动机模型复杂度和精度要求较高的领域,改进模型是一种更加有效的网络结构.展开更多
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
文摘为了解决在采用局部线性模型树(LOcal Linear MOdel Tree,LOLIMOT)辨识发动机非线性系统时,出现的辨识网络复杂和模型精度问题,提出一种将非线性自回归滑动平均模型(NARMAX)和LOLIMOT网络融合的改进神经网络结构.基于非线性自回归滑动平均模型NARMAX的思想,将原始局部子模型的线性函数替换为非线性多项式函数,并基于AIC(Akaike information criterion)显著性准则的前向选择法对非线性项按照重要性程度进行选择,将简化后的非线性函数用于构建原始LOLIMOT模型局部子模型,形成一种改进LOLIMOT网络模型.通过某航空发动机过渡态下的辨识实验表明,改进算法能够将原LOLIMOT模型复杂度降低46%左右,相对预测精度提高50%以上,验证了在对发动机模型复杂度和精度要求较高的领域,改进模型是一种更加有效的网络结构.
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.
