  We study the effect of various transfer functions on the properties of a time
series generated by a continuous-valued feed-forward network in which the next
input vector is determined from past output values. The parameter space for
monotonic and non-monotonic transfer functions is analyzed in the unstable
regions with the following main finding; non-monotonic functions can produce
robust chaos whereas monotonic functions generate fragile chaos only. In the
case of non-monotonic functions, the number of positive Lyapunov exponents
increases as a function of one of the free parameters in the model, hence, high
dimensional chaotic attractors can be generated. We extend the analysis to a
combination of monotonic and non-monotonic functions.
