During the epidemic period, our country took the lead in implementing "class suspension and non-stop teaching" to minimize the impact of the epidemic on education and improve teachers' information-based ...During the epidemic period, our country took the lead in implementing "class suspension and non-stop teaching" to minimize the impact of the epidemic on education and improve teachers' information-based teaching level. This paper studies and analyzes the construction and application model of information-based teaching resources in primary and secondary schools.展开更多
Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy b...Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)concentration.The structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural network.Furthermore,a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other methods.Originality/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP.The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations.And,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.展开更多
文摘During the epidemic period, our country took the lead in implementing "class suspension and non-stop teaching" to minimize the impact of the epidemic on education and improve teachers' information-based teaching level. This paper studies and analyzes the construction and application model of information-based teaching resources in primary and secondary schools.
基金supported by the National Natural Science Foundation of China Grant Numbers(61622301,61533002)Beijing Municipal Education Commission Science and Technology Development Program Grant Numbers(KZ201410005002,201410005001)the PhD Programs Foundation of Ministry of Education of China Grant Number(20131103110016).
文摘Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)concentration.The structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural network.Furthermore,a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other methods.Originality/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP.The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations.And,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.
