Abstract
The water influences many areas of society. Energy production, own consumption, and irrigation make use of this resource. Within the electricity production context, the flow forecasting process of the rivers that feed the electricity generation plants is very important for the success of this type of generation. Historically, neural networks have been highlighted in this type of application, in particular, the Multilayer Perceptron. Fuzzy neural networks have also been used for the same purpose. Our goal in this paper is to propose the hybridization of a fuzzy neural network that makes use of Multilayer Perceptron architecture with the Least Squares Method, to the improvement the process of monthly forecast of water flow. The neuro fuzzy network is compared to a Multilayer Perceptron network Classic through experiments and statistical tests. The results showed improvements in predictive process in most cases, suggesting that the new approach has significant potential application.
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Araujo, R., Valenca, M., Fernandes, S. (2015). A New Approach of Fuzzy Neural Networks in Monthly Forecast of Water Flow. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_47
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DOI: https://doi.org/10.1007/978-3-319-19258-1_47
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