Input-output modelling with decomposed neuro-fuzzy ARX model

Authors:
Marjan Golob;Boris Tovornik
Affiliations:
Institute of Automation, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia;Institute of Automation, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
Venue:
Neurocomputing
Year:
2008

Citing 7
Cited 2

Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Stability analysis of a three-term backpropagation algorithm

Neural Networks
Efficient decomposition methods of fuzzy relation and their application to image decomposition

Applied Soft Computing
Necessary conditions on minimal system configuration for general MISO Mamdani fuzzy systems as universal approximators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification

IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling

IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning

IEEE Transactions on Neural Networks

A Flexible Neuro-Fuzzy Autoregressive Technique for Non-linear Time Series Forecasting

KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Online identification of a neuro-fuzzy model through indirect fuzzy clustering of data space

FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.