Narrow Results

In this paper, a fuzzy logic-based approach is taken for modeling and prediction-based control of unknown chaotic system using measured input–output data obtained from the underlying system. Under this framework, a Takagi–Sugeno (TS) fuzzy system is used with a general structure of a linear combination of Gaussian basis function in conjunction with the Levenberg–Marquardt algorithm for the optimization of model parameters. A real-time one-pass learning algorithm is developed for identifying the unknown chaotic system. Based on the fuzzy model above, a predictive controller is achieved for the stabilization of the fuzzy model on unknown unstable fixed points. Several simulation examples are included to illustrate the effectiveness and the feasibility of the proposed method for both fuzzy modeling and predictive control phases.

Exponential stability of reaction–diffusion fuzzy recurrent neural networks (RDFRNNs) with time-varying delays are considered. By using the method of variational parameters, M-matrix properties and inequality technique, some delay-independent or delay-dependent sufficient conditions for guaranteeing the exponential stability of an equilibrium solution are obtained. One example is given to demonstrate the theoretical results.