In this paper,a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed,and the global convergence of this method with inexact line search is proved.Furthermore,we p...In this paper,a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed,and the global convergence of this method with inexact line search is proved.Furthermore,we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method.The global convergence of this hybrid memoryless method is proved under mild assumptions.The initial results show that these new methods are effcient for the given test problems.Especially the memoryless non-quasi-Newton method requires little storage and computation,so it is able to effciently solve large scale optimization problems.展开更多
For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a r...For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.展开更多
基金Foundation item： the National Natural Science Foundation of China （No. 60472071） the Science Foundation of Beijing Municipal Commission of Education （No. KM200710028001）.
文摘In this paper,a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed,and the global convergence of this method with inexact line search is proved.Furthermore,we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method.The global convergence of this hybrid memoryless method is proved under mild assumptions.The initial results show that these new methods are effcient for the given test problems.Especially the memoryless non-quasi-Newton method requires little storage and computation,so it is able to effciently solve large scale optimization problems.
基金This project was supported by the National Natural Science Foundation of China (69974028 60374015)
文摘For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.
