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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 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)
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)
Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain(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)
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)
In this brief, the identification problem for time-delay nonlinear system is discussed. We use a delayed dynamic neural network to do on-line identification. This neural network has dynamic series-parallel structure. The stability conditions of on-line identification are derived by Lyapunov–Krasovskii approach, which are described by linear matrix(More)
This paper considers the robust stability of neural networks with multiple delays. Based on Lyapunov stability theory and linear matrix inequality technique, some new delay independent conditions are derived to guarantee the global robust exponential stability of the equilibrium point. Furthermore, the obtained results are generalized to the interval neural(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)
Identification and control problems for unknown chaotic dynamical systems are considered. Our aim is to regulate the unknown chaos to a fixed point or a stable periodic orbit. This is realized by following two contributions. First, a dynamic neural network is used as identifier. The weights of the neural networks are adjusted by the sliding mode technique.(More)