  Chance constrained program where one seeks to minimize an objective over
decisions which satisfy randomly disturbed constraints with a given probability
is computationally intractable. This paper proposes an approximate approach to
address chance constrained program. Firstly, a single layer neural-network is
used to approximate the function from decision domain to violation probability
domain. The algorithm for updating parameters in single layer neural-network
adopts sequential extreme learning machine. Based on the neural violation
probability approximate model, a randomized algorithm is then proposed to
approach the optimizer in the probabilistic feasible domain of decision. In the
randomized algorithm, samples are extracted from decision domain uniformly at
first. Then, violation probabilities of all samples are calculated according to
neural violation probability approximate model. The ones with violation
probability higher than the required level are discarded. The minimizer in the
remained feasible decision samples is used to update sampling policy. The
policy converges to the optimal feasible decision. Numerical simulations are
implemented to validate the proposed method for non-convex problems comparing
with scenario approach and parallel randomized algorithm. The result shows that
proposed method have improved performance.
