Abstract
We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi–Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance.
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Almaksour, A., Anquetil, E. Improving premise structure in evolving Takagi–Sugeno neuro-fuzzy classifiers. Evolving Systems 2, 25–33 (2011). https://doi.org/10.1007/s12530-011-9027-0
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DOI: https://doi.org/10.1007/s12530-011-9027-0