Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results f...Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.展开更多
In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop sy...In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop system structure, the idea of virtual reference feedback tuning is proposed to avoid the identification process corresponding to the plant model. After constructing one identification cost without any knowledge of plant model, the author derives one bound on the difference between the expected identification cost and its sample identification cost under the condition that the number of data points is finite. Also the correlation between the plant input and external noise is considered in the derivation of this bound. Furthermore, the author continues to derive one probability bound to quantify this difference by using some probability inequalities and control theory.展开更多
文摘Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
基金supported by Jiangxi Provincial National Science Foundation under Grant No.20142BAB206020
文摘In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop system structure, the idea of virtual reference feedback tuning is proposed to avoid the identification process corresponding to the plant model. After constructing one identification cost without any knowledge of plant model, the author derives one bound on the difference between the expected identification cost and its sample identification cost under the condition that the number of data points is finite. Also the correlation between the plant input and external noise is considered in the derivation of this bound. Furthermore, the author continues to derive one probability bound to quantify this difference by using some probability inequalities and control theory.
