Fuzzy Kolmogorov's Network

A spline-based modification of the previously developed Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. In order to improve the approximation accuracy, cubic B-splines are substituted for triangular membership functions. The network is trained with a hybrid learning rule combining least squares estimation for the output layer and gradient descent for the hidden layer. The initialization of the NFKN is deterministic and is based on the PCA procedure. The advantages of the modified NFKN are confirmed by long-range iterated predictions of two chaotic time series: an artificial data generated by the MackeyGlass equation and a real data of laser intensity oscillations.

• Type: Conference
• Year: 2004
• Where: KES
• Authors: Vitaliy Kolodyazhniy, Yevgeniy Bodyanskiy
• Keywords: KES 2004, Long-range Iterated Predictions, Neuro-Fuzzy Kolmogorov Network, Triangular Membership Functions