Abstract
In this paper the statement and the methods for solving the comparison-based structure-parametric identification problem of multifactor estimation model are addressed. A new method that combines heuristics methods with genetic algorithms is proposed to solve the problem. In order to overcome some disadvantages of using the classical utility functions, the use of nonlinear Kolmogorov-Gabor polynomial, which contains in its composition the first as well as higher characteristics degrees and all their possible combinations is proposed in this paper. The use of nonlinear methods for identification of the multifactor estimation model showed that the use of this new technique, using as a utility function the nonlinear Kolmogorov-Gabor polynomial and the use of genetic algorithms to calculate the weights, gives a considerable saving in time and accuracy performance. This method is also simpler and more evident for the decision maker (DM) than other methods.
Highlights
In order to overcome some disadvantages of using the classical utility functions, the use of nonlinear Kolmogorov-Gabor polynomial, which contains in its composition the first as well as higher characteristics degrees and all their possible combinations is proposed in this paper
The use of nonlinear methods for identification of the multifactor estimation model showed that the use of this new technique, using as a utility function the nonlinear Kolmogorov-Gabor polynomial and the use of genetic algorithms to calculate the weights, gives a considerable saving in time and accuracy performance
Identification of the object mathematical model is to determine its parameters based on experimental investigation of the object
Summary
Identification of the object mathematical model is to determine its parameters based on experimental investigation of the object. The classical problem of identification is to determine the mathematical model y = F(x) of the object which consists of determining the transformation rules of the input x into output y or more precisely the form and parameters of operator F. Such identification is called direct because it is based on direct quantitative measurement of input and output signals of the object. Estimates given by the person to one or other properties of an object are subjective and cannot be directly measured by any physical devices In such cases, the classical methods of the direct identification are not applicable. The most convenient and widely used among these methods is the comparison-based identification [1]
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