Extreme-ANFIS (MATLAB toolbox)
The Extreme-ANFIS implements a novel neuro-fuzzy learning mechanism develop by us to accelerate and extent the applicability of conventional ANFIS (which is only useful for regression of single-input single-output). The algorithm is useful in many applications such as regression, MIMO modelling, and Multi-class classification.
Get latest version of QUEST from github repository here
- P. Jagtap (2014). Neuro-Fuzzy Systems for Modelling and Control Applications. M.Tech. Thesis.
- P. Jagtap, G. N. Pillai (2014). Comparison of extreme-ANFIS and ANFIS networks for regression problems. IEEE International Advance Computing Conference (IACC).
- G. N. Pillai, P. Jagtap, G. Nisha (2014). Extreme learning ANFIS for control applications. IEEE Symposium on Computational Intelligence in Control and Automation (CICA).
- P. Jagtap, P. Raut, G. N. Pillai, F. Kazi, N. M. Singh (2015). Extreme-ANFIS: A novel learning approach for inverse model control of Nonlinear Dynamical Systems. International Conference on Industrial Instrumentation and Control (ICIC).
- S. Thomas, G. N. Pillai, K. Pal, P. Jagtap (2016). Prediction of ground motion parameters using randomized ANFIS (RANFIS). Applied Soft Computing.