Environment humidity and temperature prediction in agriculture using Mamdani inference systems

<span>This paper presents the results of a humidity and temperature prediction model in environment for agriculture, using diffuse sets optimizing their parameters by heuristic methods, such as genetic algorithms, exact methods Quasi-Newton. It has been identified that non-specialized users could have difficulties understanding system dynamics behavior variables over time. The aim this research is obtain models with high level interpretability accuracy allows predicting values environment. use fuzzy logic to present solution great advantages highly rated interpretability. Furthermore, relating obtained qualitative categories high, medium or low, it better dynamics. Two optimization techniques are applied two different allow temperature. found best implementation involves Mamdani inference optimized Quasi-Newton algorithm uses set initial attained through previous process algorithm.</span>