Fig. 1. Basic structure of tree structured fuzzy neural networks. 그림 1. 트리 구조의 퍼지 뉴럴 네트워크 기본 구조
Table 1. Average performance index(RMSE) and number of rule for training data. 표 1. 트레이닝 데이터에 대한 평균 오차 및 규칙의 수
Table 2. Average performance index(RMSE) and number of rule for testing data. 표 2. 테스팅 데이터에 대한 평균 오차 및 규칙의 수
참고문헌
- P. Tufekci, "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods," Electrical Power and Energy Systems, vol.60, pp.126-140, 2014. DOI: 10.1016/j.ijepes.2014.02.027
- H. Kaya, P. Tufekci and F. Gurgen, "Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine," in Proc. of the Int. Conf. on Emerging Trends in Computer and Electronics Engineering, pp.13-18, 2012.
- M. Rashid, K. Kamal, T. Zafar, Z. Sheikh, A. Shah and S. Mathavan, "Energy Prediction of a Combined Cycle Power Plant using a Particle Swarm Optimization Trained Feed Forward Neural Network," in Proc. of the Int. Conf. on Mechanical Engineering, Automation and Control Systems, 2015. DOI: 10.1109/MEACS.2015.7414935
- C. W. Han, "Developing a Tree Structures of Fuzzy Controller," Advanced Science and Technology Letters, vol. 139, pp. 439-442, 2016. https://doi.org/10.14257/astl.2016.139.87
- D.E. Goldberg, Genetic algorithms in search, optimization and machine learning, Reading, MA: Addison-Wesley, 1989.
- C. W. Han, J. I. Park, "A Study on Hybrid Random Signal-based Learning and Its Applications," International Journal of Systems Science, vol.35, no.4, pp.243-253, 2004. DOI: 10.1080/00207720410001714149