Identification of parameters of interval nonlinear models of static systems using multidimensional optimization

The article proposes an approach to parametric identification of interval nonlinear models static systems based on the standard problem minimizing root mean square deviation between values ​​of modeled characteristics object and ​​belonging experimental intervals. As a result expanding parameter space by introducing additional coefficients match predicted into objective function, multidimensional optimization with multiextremal function is obtained. paper examines convergence its optimization.