A Neofuzzy neuron approach for climatic variables forecast

Abstract:

In this work it was created climatic variables pbkp_rediction models based on a modified neofuzzy neuron approach. This neofuzzy neuron approach is a simple and accurate method for obtaining climatic variables forecasting results using climatic measurements from previous days. The variables used for building the model are Temperature, Humidity, Dew Point, Wind speed, Pressure, Rain and Solar Radiation. It’s also presented as example the obtained results for temperature forecast in Ibarra, Ecuador using data from years 2012-2015.

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