A Learning Identification Algorithm and Its Application to an Environmental System

An empirical heuristic learning identification algorithm of Ivakhnenko was modified and used to model an environmental system producing high nitrate levels in agricultural drain water in the Corn Belt. The method amounts to fitting a polynomial to a multi-input single-output response surface. The modifications result in a reduced number of terms in final model equations, a decrease in computational difficulties, and other improvements in the algorithm. This method appears to be advantageous with systems characterized by complexity with many variables and parameters, ill-defined mathematical structures, and limited data. In other words, this algorithm is useful for empirically generating hypotheses about systems of which relatively little is known.