Evolutionary Chromatographic Law Identification by Recurrent Neural Nets
Alessandro Fadda, Marc Schoenauer · The MIT Press eBooks · 1995
Analytic chromatography is a physical process whose aim is the separation of the components of a chemical mixture, based on their different affinities for some porous medium through which they are percolated. This paper presents an application of evolutionary recurrent neural nets optimization to the identification of the internal law of chromatography. New mutation operators involving the parameters of a single neuron are introduced. Furthermore, the strategy for using of the different kind of mutation takes into account the past history of the neural net at hand. The first results for one- and two-component mixtures demonstrate the basic feasibility of the recurrent neural net approach. A strategy to improve the robustness of the results is presented.