Welcome to the InfoSci Platform
Reference Hub1
Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm

Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm

Didem Guleryuz
Copyright: © 2022 |Pages: 22
ISBN13: 9781799879794|ISBN10: 1799879798|ISBN13 Softcover: 9781799879800|EISBN13: 9781799879817
DOI: 10.4018/978-1-7998-7979-4.ch013
Cite Chapter Cite Chapter

MLA

Guleryuz, Didem. "Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm." Handbook of Research on Advances and Applications of Fuzzy Sets and Logic, edited by Said Broumi, IGI Global, 2022, pp. 288-309. https://doi.org/10.4018/978-1-7998-7979-4.ch013

APA

Guleryuz, D. (2022). Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm. In S. Broumi (Ed.), Handbook of Research on Advances and Applications of Fuzzy Sets and Logic (pp. 288-309). IGI Global. https://doi.org/10.4018/978-1-7998-7979-4.ch013

Chicago

Guleryuz, Didem. "Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm." In Handbook of Research on Advances and Applications of Fuzzy Sets and Logic, edited by Said Broumi, 288-309. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-7979-4.ch013

Export Reference

Mendeley
Favorite

Abstract

This chapter aims to propose prediction models to estimate Turkey's manufacturing sector's capacity utilization rate between 2008-2019 monthly basis using the adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) and particle swarm optimization (PSO) via determined indicators. The model's accuracy will be tested using some of the performance evaluation criteria, namely mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2) values were used to compare the prediction ability. The coefficient of determination for GA-ANFIS, PSO-ANFIS, and ANFIS models are 0.9787, 0.9786, and 0.9679 in the training phase and 0.9591, 0.7677, and 0.7264 in the testing phase, respectively. The study results showed that the GA-ANFIS model showed better predictive ability with the least prediction error among other models. As a result, ANFIS, whose parameters are adjusted with GA, can predict the Turkish capacity utilization rate in the manufacturing industry with high accuracy.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.