Design of enhanced MIA-GMDH learning networks

The article presents an enhanced multilayered iterative algorithm-group method of data handling (MIA-GMDH)-type network, discusses a comprehensive design methodology and carries out some numerical experiments which encompass system prediction and modelling. The method presented in this article is an enhancement of self-organising polynomial GMDH with several specific improved features – coefficient rounding and thresholding schemes and semi-randomised selection approach to pruning. The experiments carried out include representative time series prediction (gas furnace process data) and process modelling (investigating the milligrams of vitamin B2 per gram of turnip greens and drilling cutting force modelling). The results in this article show promising potential of self-organising network methodology in the field of both prediction and modelling applications.