GFRBSGenetic Fuzzy Rule-Based Systems
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The results of the performance of the GFRBS system in relation to the predicted output variable, RR, are listed in Table 5.
Once the process of analysis, design and programming of GFRBS was completed, descriptive statistics were computed to evaluate its effectiveness in improving the FIS proposed to predict RR of birds.
The functional relationships between the RR values predicted by FIS and GFRBS and values observed during the experiment period were done and the equations found for the FIS system (Eq.
However, it can be observed that in all parameters, the prediction of GFRBS showed better results than the FIS prepared, as can be seen in Table 6.
The FIS had mean percentage error of 2.77 and for GFRBS it was 0.87, thus indicating an improvement in the accuracy of prediction of RR when using the tool of genetic algorithms, as the statistical indices demonstrate.
The GFRBS interacted well with the FIS model previously developed showing an improvement in the precision of respiratory rate prediction, with the potential to be applied in models of prediction of animal physiological responses in research on the ambient area.