Analytical modeling and filtering for integrodifferential systems with unsolved derivatives

For nonlinear integrodifferential stochastic systems (IDStS) with unsolved derivatives reducible to differential stochastic systems (StS) by means of singular kernels, the following methods and algorithms are proposed: analytical modeling of normal (Gaussian) stochastic processes and analytical synthesis of normal suboptimal filters for information processing in IDStS. Both Gaussian and non-Gaussian StS white noises are considered. Quality estimation methods based on the sensitivity theory are suggested. An example with discontinuous nonlinearity is considered in details. Directions for future investigations are given.