Neuro-fuzzy modelling based on Kolmogorov's superposition: a new tool for prediction and classification
In the paper, a novel Neuro-Fuzzy Kolmogorov's Network (NFKN) is considered. The NFKN is based on the famous Kolmogorov's superposition theorem (KST). The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and simple procedures without any nonlinear operations. The validity of theoretical results and the advantages of the NFKN are confirmed by application examples: electric load forecasting, and classification of data from medical and banking domains.
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