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» Learning Nonlinear Dynamical Systems Using an EM Algorithm
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TFS
2008
129views more  TFS 2008»
14 years 8 months ago
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
Abstract--This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural netw...
Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin
IJCNN
2007
IEEE
15 years 4 months ago
Risk Assessment Algorithms Based on Recursive Neural Networks
— The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In thi...
Alejandro Chinea Manrique De Lara, Michel Parent
ICTAI
2005
IEEE
15 years 3 months ago
Hybrid Learning Neuro-Fuzzy Approach for Complex Modeling Using Asymmetric Fuzzy Sets
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (...
Chunshien Li, Kuo-Hsiang Cheng, Jiann-Der Lee
ISNN
2007
Springer
15 years 3 months ago
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
Abstract. Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence,...
José de Jesús Rubio, Wen Yu
ESANN
1998
14 years 11 months ago
Lazy learning for control design
This paper presents two local methods for the control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. The modeli...
Gianluca Bontempi, Mauro Birattari, Hugues Bersini