A fast on-line learning algorithm for multidimensional fuzzy neural networks

Authors:
Mohamed S. Ibrahim
Affiliations:
Knowledge Based Systems and Robotic Department, Mubarak City for Scientific Research and Technology Applications, Egypt
Venue:
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Year:
2005

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Abstract

This paper presents a fast on-line algorithm for constructing multidimensional fuzzy neural networks based on the possibility theory (MFNN-P).The multidimensional fuzzy neurons have a center vector and radius vector and the dimensions of these vectors are the same as the dimensions of the input vectors. The possibility measure is incorporated at the neurons in the output layer. A new on-line algorithm is developed. Simulation results show that the proposed network can deal with nonlinearities and uncertainties of the system.