In this paper, we propose a new algorithm for the estimation of the dimension of chaotic
dynamical systems using neural networks and robust location estimate. The basic idea is
that a member of a time series can be optimally expressed as a deterministic function of the
d past series values, where d is the dimension of the system. Moreover the neural networks'
learning ability is improved rapidly when the appropriate amount of information is provided
to a neural structure which is as complex as needed. To estimate the dimension of a …