Characterization of the convergence of stationary Fokker–Planck learning

The convergence properties of the stationary Fokker–Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empirical arguments for the characterization of convergence of the estimation in the case of separable and nonseparable nonlinear optimization problems are given. Some implications of the convergence of stationary Fokker–Planck learning for the inference of parameters in artificial neural network models are outlined.