Title :
Stability analysis of nonlinear systems using high order derivatives of universal learning networks
Author :
Hirasawa, Kotaro ; Yu, Yunqing ; Hu, Jinglu ; Murata, Junichi
Author_Institution :
Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan
Abstract :
In this paper, a stability analysis method based on the higher order derivatives of universal learning networks is proposed. In the proposed method, the following are proposed. First, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Next, the locally asymptotically stable region, where asymptotic stability is secured approximately, is obtained by comparing the first order derivatives and higher order derivatives
Keywords :
asymptotic stability; control system analysis; learning systems; neural nets; nonlinear systems; asymptotic stability; high order derivatives; nonlinear systems; universal learning networks; Asymptotic stability; Delay effects; Differential equations; H infinity control; Input variables; Lyapunov method; Nonlinear dynamical systems; Nonlinear systems; Stability analysis; Systems engineering and theory;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939544