I would like to use all the three soft computing methods in my application. Can anyone help me in suggesting some papers which uses this hybrid method? Thanks in advance.
Here is a conference paper in which a neuro-fuzzy controller genetically enhanced was developed for a hexapod robot:
Pedro Ponce, Fernando Ramírez, Verónica Medina, "A novel neuro-fuzzy controller genetically enhanced using LabVIEW"; DOI: 10.1109/IECON.2008.4758186
I think following papers are addressed to similar problems
Melin, Patricia, and Oscar Castillo. "Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach." Industrial Electronics, IEEE Transactions on 48.5 (2001): 951-955.
Aguilar, Leocundo, Patricia Melin, and Oscar Castillo. "Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach." Applied Soft Computing 3.3 (2003): 209-219.
Mitra, Sushmita, and Yoichi Hayashi. "Neuro-fuzzy rule generation: survey in soft computing framework." Neural Networks, IEEE Transactions on 11.3 (2000): 748-768.
but I don't know how you want to use this three technique and what's your problem?
You can use genetic algorithms for finding the suitable coefficients of the output layer in ANFIS; or better, you can use genetic algorithms to optimize the number of neurons in ANFIS.
- Badges
- Science topic
- Similar topics
- Agriculture