Evolutionary Chromatographic Law Identification by Recurrent Neural Nets

Analytic chromatography is a physical process whose aim is the separation of the components of a chemical mixture, based on their different aanities for some porous medium through which they are percolated. This paper presents an application of evolutionary recurrent neural nets optimization to the identiication 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 diierent kind of mutation takes into account the past history of the neural net at hand. The rst 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 .