  Stochastic dynamical systems often contain nonlinearities which make it hard
to compute probability density functions or statistical moments of these
systems. For the moment computations, nonlinearities in the dynamics lead to
unclosed moment dynamics; in particular, the time evolution of a moment of a
specific order may depend both on moments of order higher than it and on some
nonlinear function of other moments. The moment closure techniques are used to
find an approximate, close system of equations the moment dynamics. In this
work, we extend a moment closure technique based on derivative matching that
was originally proposed for polynomial stochastic systems with discrete states
to continuous state stochastic systems to continuous state stochastic
differential equations, with both polynomial and trigonometric nonlinearities.
We validate the technique using two examples of nonlinear stochastic systems.
