Identifying chaotic attractors with neural networks

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