SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions

GIORDANO, PABLO C.; GOICOECHEA, HÉCTOR C.; OLIVIERI, ALEJANDRO C.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

ELSEVIER SCIENCE BV

Año: 2017 vol. 171 p. 198 - 206

0169-7439

SRO_ANN, a MatLab® toolbox for implementing multiple surface response optimization by artificial neural networks(SRO_ANN) is presented. Radial basis functions, a type of artificial neural networks, are applied through aneasily managed graphical user interface. A detailed description of the interface is provided, including a simulatedand two literature examples which allow one to show the potentiality of the software. The discussed experimentalexamples correspond to: (1) the maximization of the research octane number (RON) of fuels, influenced by threefactors (reaction temperature, operating pressure and low liquid hourly space velocity), and (2) the optimizationof the calcification process for diced tomatoes, evaluated through three different responses (calcium content,firmness and pH), which are affected by three factors (calcium concentration, solution temperature and treatmenttime). The results show that the application of a nonparametric tool can enhance the performance of optimizationmodeling tasks.