In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapu...In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.展开更多
In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involv...In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network(RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization(PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.展开更多
基金This work is supported by Fundamental Research Funds for the Central Universities （Grant Nos. XDJK2016BC137, SWU116004）, Natural Science Foundation of China （Grant Nos. 61374078, 61403313, 61633011）. This publication was made possible by NPRP Grant NPRP 7-1482-1-278 from the Qatar National Research Fund （a member of Qatar Foundation）.
文摘In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.
基金supported by the Fundamental Research Funds for the Central Universities (No. XDJK2019B010)the Natural Science Foundation of China(No. 61773320)+2 种基金the Natural Science of Chongqing Science and Technology Commission (CSTC)(No. cstc2018jcyj AX0583, No. cstc2018jcyj AX0810)the Research Foundation of Key Laboratory of Machine Perception and Children’s Intelligence Development funded by Chongqing University of Education (CQUE)(No. 16xjpt07)the Foundation of Chongqing University of Education (No. KY201702A)。
文摘In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network(RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization(PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.
