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.

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Get latest version of QUEST from github repository here

  1. P. Jagtap (2014). Neuro-Fuzzy Systems for Modelling and Control Applications. M.Tech. Thesis.
  2. P. Jagtap, G. N. Pillai (2014). Comparison of extreme-ANFIS and ANFIS networks for regression problems. IEEE International Advance Computing Conference (IACC).
  3. G. N. Pillai, P. Jagtap, G. Nisha (2014). Extreme learning ANFIS for control applications. IEEE Symposium on Computational Intelligence in Control and Automation (CICA).
  4. 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).
  5. S. Thomas, G. N. Pillai, K. Pal, P. Jagtap (2016). Prediction of ground motion parameters using randomized ANFIS (RANFIS). Applied Soft Computing.
Pushpak Jagtap
Postdoctoral Researcher