  The properties of time series generated by a perceptron with monotonic and
non-monotonic transfer function, where the next input vector is determined from
past output values, are examined. Analysis of the parameter space reveals the
following main finding: a perceptron with a monotonic function can produce
fragile chaos only whereas a non-monotonic function can generate robust chaos
as well. For non-monotonic functions, the dimension of the attractor can be
controlled monotonically by tuning a natural parameter in the model.
