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Neural Networks for Predicting Prices of Sugarcane Derivatives

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Abstract

Brazil is one of the largest sugarcane producers. It directly affects job creation and national gross domestic product, as well as bringing a large amount of foreign money to the country. Hence, this paper proposes investigating the application of neural networks—multilayer perceptron, extreme learning machines (ELM) and echo state networks (ESN)—for predicting the following cane derivatives prices: sugar, hydrous ethanol, and anhydrous ethanol. The main characteristic of ELM and ESN is their simple and fast training process, being based on the analytic calculation of the coefficients of a linear combiner. Their intermediate layer stands untuned, and the weights in this layer are randomly and independently defined. The computational results show that the application of the ELM achieved the best overall results, showing that they are viable candidates for these kinds of problems.

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Authors and Affiliations

  1. Department of Electronic Engineering, Federal University of Technology - Paraná, Monteiro Lobato Av., km 4, s/n, Ponta Grossa, PR, 84016-210, Brazil

    Natália Silva, Sérgio Okida, Sérgio L. Stevan Jr. & Hugo Siqueira

  2. School of Medicine, Federal University of Mato Grosso do Sul, Cidade Universitaria, s/n, Unit 09, Campo Grande, MS, 79070-900, Brazil

    Igor Siqueira

Authors
  1. Natália Silva
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  2. Igor Siqueira
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  3. Sérgio Okida
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  4. Sérgio L. Stevan Jr.
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  5. Hugo Siqueira
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Correspondence to Hugo Siqueira.

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Silva, N., Siqueira, I., Okida, S. et al. Neural Networks for Predicting Prices of Sugarcane Derivatives. Sugar Tech 21, 514–523 (2019). https://doi.org/10.1007/s12355-018-0648-5

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  • DOI: https://doi.org/10.1007/s12355-018-0648-5

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