References
- K Tharani, N Kumar, V Srivastava, S Mishra, M Pratyush Jayachandran, “Machine learning models for renewable energy forecasting”, Journal of Statistics and Management Systems, Vol. 23, No.1 pp.171-180, Feb 2020. doi: https://doi.org/10.1080/09720510.2020.1721636
- Martin T.hagan and Mohan B. menhaj-Training feedforward networks with marquardt algorithm,IEEE transactions on neural networks,Vol.5, No.6,November 1994.
- T. G. Barbounis, J. B. Theocharis, P. S. Dokopoulosy, “Long-term wind speed and power forecasting using local recurrent neural network models” IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 273-284, Mar.2006. doi: https://doi.org/10.1109/TEC.2005.847954
- G. N. Kariniotakis, G. S. Stavrakakis, E. F. Nogaret, “Wind power forecasting using advanced neural networks models”, IEEE Trans. Energy Convers., vol. 11, no. 4, pp. 762-767, Dec. 1996. doi: https://doi.org/10.1109/60.556376
- N. A. More, M. C. Deo, “Forecasting wind with neural networks” Marine Structures, vol. 16, pp. 35-49, 2003. doi: https://doi.org/10.1016/S0951-8339(02)00053-9
- Sudha, K. and Bai, V.T. (2017) ‘An adaptive approach for the fault tolerant control of a nonlinear system’, Int. J. Automation and Control, Vol. 11, No. 2, pp.105–123. doi: https://doi.org/10.1504/IJAAC.2017.083299
- José Vieira, Fernando Morgado Dias and Alexandre Mota. March 2004. Neuro-Fuzzy Systems: A Survey. 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia.
- Adrian Tam. 2021. A Gentle Introduction to Particle Swarm Optimization. In, Machine Learning Mastery.
- Ajay Singh Yadav, Prerna Maheshwari and Anupam Swami. 2016. Analysis of Genetic Algorithm and Particle Swarm Optimization for Warehouse with Supply Chain Management in Inventory Control. International Journal of Computer Applications, 154(5).