Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
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
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In this paper we present a new continuous-time recurrent neurofuzzy network structure for modeling and identification of a class of nonlinear systems, using a training algorithm motivated from previous works in adaptive observers. Using only output measurements and the knowledge of an excitation input signal, the proposed network is trained by generating estimates of an ideal network and jointly identifying its parameters. The objective is to make the network to dynamically behave as the plant. The stability of the network and the convergence of the training algorithm are established based on the Lyapunov stability theory. Two numerical examples and an experimental result are included to demonstrate the effectiveness of the proposed method.