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
Equations that can predict worsted fabrics’ properties such as bending, shearing, compression, surface and tension, were achieved by means of theoretical and experimental studies. By combining these equations with Ka...Equations that can predict worsted fabrics’ properties such as bending, shearing, compression, surface and tension, were achieved by means of theoretical and experimental studies. By combining these equations with Kawabata’s hand and silhouette evaluation methods, a software system was established. Then the mechanical properties, hand and silhouette of a fabric can be predicted quickly and accurately in terms of fiber configurations, yarn and fabric structures. The predictive result if unsatisfied can be revised by the function of “Help for designing modification”.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.
文摘Equations that can predict worsted fabrics’ properties such as bending, shearing, compression, surface and tension, were achieved by means of theoretical and experimental studies. By combining these equations with Kawabata’s hand and silhouette evaluation methods, a software system was established. Then the mechanical properties, hand and silhouette of a fabric can be predicted quickly and accurately in terms of fiber configurations, yarn and fabric structures. The predictive result if unsatisfied can be revised by the function of “Help for designing modification”.
