Paulo Gil
ABSTRACT
In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.
- R. Babuska and H. Verbruggen. Neuro-fuzzy methods for nonlinear system identification. Annual reviews in control, 27(1):73--85, 2003. Google Scholar
Cross Ref
- L. Rutkowski. Flexible neuro-fuzzy systems: structures, learning and performance evaluation, volume 771. Springer Science & Business Media, 2006.Google Scholar
- M. Z. Zgurovsky and Y. P. Zaychenko. The fundamentals of computational intelligence: System approach, 2016.Google Scholar
- R. Dash and P. Dash. Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified technique. Expert Systems with Applications, 52:75--90, 2016. Google ScholarDigital Library
- J. Zhang and A. J. Morris. Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Transactions on Neural Networks, 10(2):313--326, 1999. Google ScholarDigital Library
- J. Dunik, M. Simandl, and O. Straka. Unscented Kalman filter: aspects and adaptive setting of scaling parameter. IEEE Transactions on Automatic Control, 57(9):2411--2416, 2012. Google ScholarCross Ref
- S. Julier and J. Uhlmann. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401--422, Mar 2004. Google Scholar
- B. Teixeira, L. A. Tôrres, L. Aguirre, and D. S. Bernstein. On unscented Kalman filtering with state interval constraints. Journal of Process Control, 20(1):45--57, 2010. Google ScholarCross Ref
Recommendations
Online non-affine nonlinear system identification based on state-space neuro-fuzzy models
This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel ...
Interval type-2 neuro-fuzzy system with implication-based inference mechanism
The system uses interval type-2 fuzzy sets in premises and consequences of rules.The system uses several interval type-2 fuzzy implications.The system applies logical interpretation to fuzzy rules.The paper is accompanied by numerical examples.The ...
Knowledge-based parameter identification of TSK fuzzy models
Linear/1st order Takagi-Sugeno-Kang (TSK) fuzzy models are widely used to identify static nonlinear systems from a set of input-output pairs. The synergetic integration of TSK fuzzy models with artificial neural networks (ANN) has led to the emergence of ...
Comments