Problems of Further Development of the Group Method of Data Handling Algorithms. Part I
Alexey G. Ivakhnenko, G. A. Ivakhnenko · 2000
The GMDH algorithms for solving interpolation problems of artificial intelligence differ from each other in the form of the reference function and the iteration rules of the multilayer model structure. In some multilayer algorithms, the number of terms in the iteration rule is constant, which leads to the skipping of some models. In the algorithm called combinatorial, the iteration rule increases by one term when passing to each next row, which ensures an exhaustive search through all of the equations. For exact and complete data, the minimum of the external criterion is nonsharp, and to determine an optimal method, extrapolation of the locus of points of the minimum of the external criterion should be performed. A comparison of linear, polynomial, and ratio-polynomial (with respect to the coefficients) functions may give a method for improving the accuracy of problem solutions. To reduce computational time, a threshold GMDH algorithm is developed which preliminarily estimates the effectiveness of the input variables at the information level and searches for model-candidates based on the most effective input variables (arguments or features).