Optimised Subtractive Clustering for Neuro-Fuzzy Models

This paper presents results obtained when developing more efficient clustering methods for neurofuzzy model identification. Nelder-Mead optimisation is applied for fine tuning subtractive clustering based rule selection parameters. The performance is tested with various data sets against each other and earlier works done. The proposed method seems to produce more accurate models with fewer rules. Key-Words: Optimization, Subtractive clustering, Neuro-Fuzzy model, Nelder-Mead, Identification

[1]  J.-S.R. Jang,et al.  Predicting chaotic time series with fuzzy if-then rules , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[2]  S. Chiu,et al.  A cluster estimation method with extension to fuzzy model identification , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  Margaret H. Wright,et al.  Direct search methods: Once scorned, now respectable , 1996 .

[4]  J.-S.R. Jang,et al.  Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[5]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[6]  Dimiter Driankov,et al.  Fuzzy model identification - selected approaches , 1997 .

[7]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[8]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[9]  Joey Sing Yee Tan,et al.  Fuzzy Inference System , 2019, Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation.