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Neural networks have stimulated the interest of more and more scientists and engineers who have to cope with the control of nonlinear systems. The appeal is based on theoretical capabilities of neural networks to approximate arbitrary well continuous functions in compact sets. The books devoted to the control by neural networks are few. Therefore the(More)
—In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification terms must be included in order to guarantee Lyapunov stability. This paper suggests new learning laws for Mamdani and Takagi–Sugeno–Kang type fuzzy(More)
In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used(More)
—Since knowledge in expert system is vague and modified frequently, expert systems are fuzzy and dynamic systems. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. Aiming at this object, a generalized fuzzy Petri net model is proposed in this paper, it(More)
Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system(More)
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach(More)
Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability(More)
— Despite of good theoretic foundations and high classification accuracy of support vector machine (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel two stages SVM classification approach for large data sets by randomly selecting training data. The first(More)
— In this paper we propose a novel on-line clustering approach which can be applied for nonlinear system identification. Both structure and parameters of fuzzy neural networks are updated on-line. The new clustering method for the structure identification can divide input/output data into different groups (rule number) by on-line data. For the parameter(More)