The use of nonlinear probabilistic criteria to solve adaptive filtering problems

The paper presents a general method for solution of the adaptive parametric filtering problem requiring much less computational effort at a potentially greater accuracy of estimation-identification as compared with the traditional methods. The accuracy increase is obtained at the expense of the use of more general probabilistic criteria instead of the mean -square criterion. An example of identifying a stochastic nonlinear object is given.