Research Article

An application for the PID-based optimizer loop: Estimation of the annual production regression models of Malatya’s apricot

Volume: 13 Number: 3 September 30, 2022
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An application for the PID-based optimizer loop: Estimation of the annual production regression models of Malatya’s apricot

Abstract

In this study, a data analysis application for the PID-based optimizer loop, which was previously proposed in a former study, is carried out. In this application, quadratic and cubic polynomial regression models were obtained for the estimation of annual apricot production by using the yearly total apricot production data of Malatya between 1991 and 2020. In addition, an average of these regression model estimations was calculated to increase estimation reliability. Annual apricot production amount was estimated by using the regression models obtained with the PID-based optimizer system between 2021-2025. The results were compared with the results obtained with the Matlab curve fitting toolbox.

Keywords

References

  1. K. J. Åström and T. Hägglund, “The future of PID control,” Control Eng. Pract., vol. 9, no. 11, pp. 1163–1175, Nov. 2001.
  2. R. Vitfhal, P. Sunthar, and C. H. D. Rao, “The Generalized Proportional-Integral-Derivative ( PID ) Gradient Descent Back Propagation Algorithm,” Neural Networks, vol. 8, no. 4, pp. 563–569, 1995.
  3. Y. H. Zweiri, J. F. Whidborne, and L. D. Seneviratne, “A three-term backpropagation algorithm,” Neurocomputing, vol. 50, pp. 305–318, 2003.
  4. Y. H. Zweiri, L. D. Seneviratne, and K. Althoefer, “Stability analysis of a three-term backpropagation algorithm,” Neural Networks, vol. 18, pp. 1341–1347, 2005.
  5. X. Jing and L. Cheng, “An Optimal PID Control Algorithm for Training Feedforward Neural Networks,” IEEE Trans. Ind. Electron., vol. 60, no. 6, pp. 2273–2283, 2013.
  6. G. Zeng, X. Xie, M. Chen, and J. Weng, “Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems,” Swarm Evol. Comput., vol. 44, pp. 320–334, 2019.
  7. J. Li, Y. Yuan, T. Ruan, J. Chen, and X. Luo, “A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model,” Neurocomputing, vol. 427, pp. 29–39, 2021.
  8. H. Wang, Y. Luo, W. An, and Q. Sun, “PID Controller-Based Stochastic Optimization Acceleration for Deep Neural Networks,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 12, pp. 5079–5091, 2020.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Publication Date

September 30, 2022

Submission Date

July 19, 2022

Acceptance Date

August 28, 2022

Published in Issue

Year 2022 Volume: 13 Number: 3

IEEE
[1]F. N. Deniz, “An application for the PID-based optimizer loop: Estimation of the annual production regression models of Malatya’s apricot”, DUJE, vol. 13, no. 3, pp. 511–516, Sept. 2022, doi: 10.24012/dumf.1145295.