Memetic Type-2 Fuzzy System Learning for Load Forecasting
- 10.2991/ifsa-eusflat-15.2015.128How to use a DOI?
- Interval type-2 fuzzy systems, memetic learning, load forecasting.
This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system’s parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day-ahead load forecasting.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Iván Castro León AU - Philip C. Taylor PY - 2015/06 DA - 2015/06 TI - Memetic Type-2 Fuzzy System Learning for Load Forecasting BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 909 EP - 916 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.128 DO - 10.2991/ifsa-eusflat-15.2015.128 ID - CastroLeón2015/06 ER -