Analytical modeling of normal processes in stochastic systems with integral nonlinearities (II)

General methodological and algorithmical support for analytical modeling of normal processes in differential stochastic systems (StS) with probabilistic integral nonlinearities (PIN) and Wiener and Poisson noises is presented. The support is based on the method of normal approximation (MNA) and the method of statistical linearization (MSL). Probabilistic integral nonlinearities were approximated by power and Hermite series. The MSL and MNA coefficients for PIN described by exponential, gamma, and $\chi^2$-distributions are presented. The necessary information about the function of the parabolic cylinder is also presented. Two test examples are considered. Some generalizations are mentioned.