Abstract
An industrial process of corn biscuit manufacture had been simulated and optimized by RBF and MLP networks, in this work. Data bases of the cooking process, in continuous, indirect, gas-fired oven (industrial scale) were used to the best understanding the behavior of the control variables. Weight and thickness of a corn biscuit were used as responses. After training, neuro Fuzzy model was applied on experimental data. Results showed that Neuro Fuzzy network has a good response surface, for both RBF and MLP networks; it showed low values of average error. The RBF and MLP network architectures showed satisfactory results, in line with the experimental data. Optimization showed that is possible to obtain one biscuit with 4.64-5.00 g and 5.14-5.65 mm of thickness and wrapping up in a package of 400 g of net weight. This allows the maintenance of the product of the damaged norms of quality.
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