Title :
Identifying chaotic attractors with neural networks
Author_Institution :
Res. Center, Daimler-Benz AG, Ulm, Germany
Abstract :
Behavior of chaotic systems cannot be exactly forecast for all state variables by identified models since a deviation in model parameters leads to exponential forecast error. However, under certain conditions a model can be identified that possesses the same strange attractor. A procedure for identifying such models is presented. This procedure is based on error volume evaluation, instead of additive squared error
Keywords :
chaos; identification; neural nets; additive squared error; chaotic attractor identification; chaotic systems; error volume evaluation; exponential forecast error; neural networks; strange attractor; Chaos; Least squares methods; Linear systems; Mathematical model; Neural networks; Nonlinear systems; Predictive models; State-space methods; System identification; Topology;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487831
