In this paper, we introduce the pre-frame operator Q for the g-frame in a complex Hilbert space, which will play a key role in studying g-frames and g-Riesz bases etc. Using the pre-frame operator Q, we give some nece...In this paper, we introduce the pre-frame operator Q for the g-frame in a complex Hilbert space, which will play a key role in studying g-frames and g-Riesz bases etc. Using the pre-frame operator Q, we give some necessary and sufficient conditions for a g-Bessel sequence, a g-frame, and a g-Riesz basis in a complex Hilbert space, which have properties similar to those of the Bessel sequence, frame, and Riesz basis respectively. We also obtain the relation between a g-frame and a g-Riesz basis, and the relation of bounds between a g-frame and a g-Riesz basis. Lastly, we consider the stability of a g-frame or a g-Riesz basis for a Hilbert space under perturbation.展开更多
This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unkn...This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.展开更多
基金the Natural Science Foundation of Fujian Province,China (No.Z0511013)the Education Commission Foundation of Fujian Province,China (No.JB04038)
文摘In this paper, we introduce the pre-frame operator Q for the g-frame in a complex Hilbert space, which will play a key role in studying g-frames and g-Riesz bases etc. Using the pre-frame operator Q, we give some necessary and sufficient conditions for a g-Bessel sequence, a g-frame, and a g-Riesz basis in a complex Hilbert space, which have properties similar to those of the Bessel sequence, frame, and Riesz basis respectively. We also obtain the relation between a g-frame and a g-Riesz basis, and the relation of bounds between a g-frame and a g-Riesz basis. Lastly, we consider the stability of a g-frame or a g-Riesz basis for a Hilbert space under perturbation.
基金supported by National Outstanding Youth Science Foundation(61125306)National Natural Science Foundation of Major Research Plan(91016004,61034002)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education of China(20110092110020)Open Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering(Southeast University)Ministry of Education(MCCSE2013B01)
基金partially supported by the National Natural Science Foundation of China(61703402,61374048)
文摘This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
