The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining...The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.展开更多
In the past,scholars have divided the history of Chinese papermaking into different stages based on the development of ancient papermaking technology,emphasizing the development and progress of papermaking in differen...In the past,scholars have divided the history of Chinese papermaking into different stages based on the development of ancient papermaking technology,emphasizing the development and progress of papermaking in different historical periods but paying less attention to the changes in the form of paper.Here,the stages are defined based on changes in use and function rather than technological developments.When this approach is combined with the history of printing,books,calligraphy,and painting,the history of Chinese papermaking can be divided into the writing paper,writing and printing paper,printing paper,and calligraphy and painting paper periods.Different periods of paper have significant differences in texture,form,and performance owing to their different applications.This significant difference provides a reference for the identification of ancient papers and reveals the internal connection between the history of printing,books,calligraphy,painting,and papermaking.展开更多
基金support received from the National Natural Science Foundation of China(Grant No.62073206)Technical Innovation Guidance Project of Shaanxi Province(Grant No.2020CGHJ-007).
文摘The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.
文摘In the past,scholars have divided the history of Chinese papermaking into different stages based on the development of ancient papermaking technology,emphasizing the development and progress of papermaking in different historical periods but paying less attention to the changes in the form of paper.Here,the stages are defined based on changes in use and function rather than technological developments.When this approach is combined with the history of printing,books,calligraphy,and painting,the history of Chinese papermaking can be divided into the writing paper,writing and printing paper,printing paper,and calligraphy and painting paper periods.Different periods of paper have significant differences in texture,form,and performance owing to their different applications.This significant difference provides a reference for the identification of ancient papers and reveals the internal connection between the history of printing,books,calligraphy,painting,and papermaking.
