Stability of adaptive systems: passivity and averaging analysis
Stability of adaptive systems: passivity and averaging analysis
Stable adaptive systems
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Nonlinear Control Systems
Fuzzy Control
Dynamic neural observers and their application for identification and purification of water by ozone
Automation and Remote Control
A new recurrent neurofuzzy network for identification of dynamic systems
Fuzzy Sets and Systems
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic non-Singleton fuzzy logic systems for nonlinear modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
Genetic algorithm based structure identification for feedback control of nonlinear MIMO systems
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Uncertain nonlinear system modeling and identification using belief rule-based systems
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
| Hi-index | 0.00 |
This brief presents a structure for black-box identification based on continuous-time recurrent neurofuzzy networks for a class of dynamic nonlinear systems. The proposed network catches the dynamics of a system by generating its own states, using only input and output measurements of the system. The training algorithm is based on adaptive observer theory, the stability of the network, the convergence of the training algorithm, and the ultimate bound on the identification error as well as the parameter error are established. Experimental results are included to illustrate the effectiveness of the proposed method.