  We propose an algorithm for designing optimal inputs for on-line Bayesian
identification of stochastic non-linear state-space models. The proposed method
relies on minimization of the posterior Cram\'er Rao lower bound derived for
the model parameters, with respect to the input sequence. To render the
optimization problem computationally tractable, the inputs are parametrized as
a multi-dimensional Markov chain in the input space. The proposed approach is
illustrated through a simulation example.
