An Adaptive Learning Algorithm for a Neuro-fuzzy Network

Authors:
Yevgeniy Bodyanskiy;Vitaliy Kolodyazhniy;Andreas Stephan
Affiliations:
-;-;-
Venue:
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Year:
2001

Citing 2
Cited 1

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural networks: a systematic introduction

Neural networks: a systematic introduction

Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper addresses the problem of online adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both antecedent and consequent parts of fuzzy rules is proposed. The algorithm is derived from the well-known Marquardt procedure and uses approximation of the Hessian matrix. A characteristic feature of the proposed algorithm is that it does not require time-consuming matrix operations. Simulation results illustrate apcpaltiion to adaptive identification of a nonlinear plant and nonlinear time series prediction.