PID tabanlı optimize edici döngüsü için bir uygulama: Malatya kayısısının yıllık üretim regresyon modellerinin tahmini

Year 2022, Volume: 13 Issue: 3, 511 - 516, 30.09.2022

Furkan Nur Deniz

https://doi.org/10.24012/dumf.1145295

Abstract

Bu çalışmada daha önceki bir çalışmada önerilen PID tabanlı optimizer döngüsü için bir veri analizi uygulaması yapılmıştır. Bu uygulamada Malatya ilinin 1991-2020 yılları arasındaki yıllık toplam kayısı üretim verileri kullanılarak yıllık kayısı üretiminin tahmini için kuadratik ve kübik polinom regresyon modelleri elde edilmiştir. Ayrıca tahmin güvenirliğini artırmak için bu regresyon model tahminlerinin ortalaması hesaplanmıştır. . 2021-2025 yılları arasında PID tabanlı optimizer sistemi ile elde edilen regresyon modelleri kullanılarak yıllık kayısı üretim miktarı tahmin edilmiştir. Sonuçlar, Matlab eğri uydurma araç kutusu ile elde edilen sonuçlarla karşılaştırılmıştır.

Keywords

PID tabanlı optimize edici, doğrusal olmayan optimizasyon, regresyon modeli

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

PID-based optimizer, nonlinear optimization, regression model

References

• K. J. Åström and T. Hägglund, “The future of PID control,” Control Eng. Pract., vol. 9, no. 11, pp. 1163–1175, Nov. 2001.
• 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.
• Y. H. Zweiri, J. F. Whidborne, and L. D. Seneviratne, “A three-term backpropagation algorithm,” Neurocomputing, vol. 50, pp. 305–318, 2003.
• 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.
• 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.
• 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.
• 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.
• 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.
• D. Wang, M. Ji, Y. Wang, H. Wang, and L. Fang, “Spi-Optimizer: An Integral-Separated PI Controller For Stochastic Optimization,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, no. 61722209, pp. 2129–2133.
• B. B. Alagoz, F. N. Deniz, and M. Koseoglu, “Systems & Control Letters An efficient PID-based optimizer loop and its application in De Jong ’ s functions minimization and quadratic regression problems,” Syst. Control Lett., vol. 159, p. 105090, 2022.
• “MATLAB Release 2020b, The MathWorks, Inc., Natick, Massachusetts, United States.” 2020.
• B. K. Topuz, M. Bozoğlu, U. Başer, and N. A. Eroğlu, “Forecasting of Apricot Production of Turkey by Using Box-Jenkins Method,” Turkish J. Forecast., vol. 02, no. 2, pp. 20–26, 2018.
• A. S. Uzundumlu, T. Karabacak, and A. Ali, “Apricot production forecast of the leading countries in the period of 2018-2025,” Emirates J. Food Agric., vol. 33, no. 8, pp. 682–690, 2021.
• K. Ogata, Modern Control Engineering, Prentice H. 2002.
• K. Çatı and S. Yıldız, “Türkiye’de Kuru Kayısı Üretim Ve Pazarlama Problemleri Ve Çözüm Önerileri,” İktisadi ve İdari Bilim. Derg., vol. 21, no. 1, 2007.
• Fırat Kalkınma Ajansı, “Kayisi araştirma raporu,” 2010.
• Tarımsal Ekonomi Ve Politika Geliştirme Enstitüsü TEPGE, “Ürün Raporu-Kayısı,” 2021.
• D. Ari and B. B. Alagoz, “Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming,” in 2021 International Conference on Information Technology (ICIT), 2021, pp. 382–386.