The Comparison of the Monte-Carlo Method and Neural Networks Algorithms in Nonlinear Estimation Problems

The paper compares the algorithms based on neural networks and the Monte-Carlo method as applied to nonlinear estimation problems solved in the framework of the Bayesian approach. Two variants are considered. The first variant is a search of optimal estimates that are conditional mathematical expectations and, in a general case, depend on measurements in a nonlinear way. The second variant involves linear optimal estimates. In designing them, the root-mean-square criterion is minimized in the class of estimates that are linearly dependent on measurements. The comparison results are discussed.