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It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are(More)
This paper considers the problem of using approximate methods for realizing the neural controllers for nonlinear multivariable systems. In [1] the NARMA-L1 and NARMA-L2 models were introduced as approximations of he NARMA model used for the representation of a SISO nonlinear dynamical systems. The advantage obtained from using NARMA-L1 and NARMA-L2 models(More)
An attempt is made to indicate how practically viable controllers can be designed using neural networks, based on results in nonlinear control theory. The problem of stabilization of a dynamical system around an equilibrium point when the state of the system is accessible is considered. Simulation results are included to complement the theoretical(More)
In this paper, adaptive control of a class of nonlinear discrete time dynamical systems with boundedness of all signals is established by using a linear robust adaptive controller and a neural network based nonlinear adaptive controller, and switching between them by a suitably defined switching law. The linear controller, when used alone, assures(More)
The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods(More)
response of the frequency estimation loop and simplified its design. The estimates were unbiased and ripple-free when the signal contained no noise and the parameters of the signal were constant. A modified version of the algorithm provided improvements for situations in which the fundamental component of the signal could become small, or vanish for some(More)