Title :
Stability analysis of nonlinear neural network models
Author_Institution :
Dept. of Math., Claremont Graduate Sch., CA, USA
Abstract :
The problem of stability for nonlinear neural networks is addressed in this paper. By means of the Lyapunov function of Lurie type, new classes of stability conditions for general neural network models are presented. The stability analysis here is global in the space of neuronal activations. An illustrated example is given
Keywords :
Lyapunov methods; asymptotic stability; neural nets; Lurie Lyapunov function; asymptotic stability; global analysis; neural network models; neuronal activations; nonlinear neural network; stability analysis; stability conditions; Asymptotic stability; Cellular neural networks; Hopfield neural networks; Large-scale systems; Lyapunov method; Mathematics; Neural networks; Neurons; Signal processing; Stability analysis;
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
DOI :
10.1109/ISCAS.1996.542156
