Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons

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“…New hybrid architectures of iterative GMDH algorithms were constructed as a generalization of algorithmic structures of multilayered, relaxational and combinatorial types, based on which the generalized iterative algorithm GIA GMDH [67] was developed as a neural network with active neurons in the form of the COMBI algorithm for automatic adjustment of a neuron complexity.…”

Comparative Studies of Self-organizing Algorithms for Forecasting Economic Parameters

“…Finally, the various methods of modeling that can be classified as inductive are highlighted by the group method of data handling (GMDH), allowing to build models directly on data sampling without attracting additional a priori information [12,13].…”

“…The name of the relaxation iterative algorithm (RIA) corresponds to the analogy with optimization algorithms. In this algorithm, in each row, the intermediate arguments are combined in pairs with the original [21], which prevents loss of informative arguments, with such possible variants of linear, bilinear, or partial quadratic descriptions:…”

Metalearning as One of the Task of the Machine Learning Problems

“…In [18,19], the problems of metamodeling and metalearning based on an inductive approach are compared: metamodeling is a generalization of some information about a group of objects in a particular model, and metalearning is the use of accumulated experience about the best way to determine the structure and parameters of such a model. It is shown that the generalized iterative algorithm GIA GMDH [20] allows you to build mathematical models of specific objects. To use this software metamodel, it needs to set parameters, and we can get a specific model.…”

“…Stages or blocks from which the system will be composed [20]: work with various data-bases and knowledges bases; data preprocessing, selection of class of task and data analysis; preliminary (reconnaissance) data analysis, selection of an object class, function class, data conversion depending on the purpose of modelling; task formation: selection of external criteria, parameter estimation methodology, structure generator, solution algorithm formation, parameter management task; solution; creating a model, checking the adequacy of the model (for example, in an exam), analysing the results, building many models; application of the results.…”