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Titlebook: Nonlinear Identification and Control; A Neural Network App G. P. Liu Book 2001 Springer-Verlag London 2001 Adaptive control.Control.Control

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楼主: Covenant
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Nonlinear Adaptive Neural Control, control problems are the multilayer perceptron neural networks with sigmoidal units (Psaltis . 1988; Miller . 1990; Narendra and Parthasarathy, 1990) and the radial basis function neural networks (Powell, 1987; Niranjan and Fallside, 1990; Poggio and Girosi, 1990a).
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Sequential Nonlinear Identification,al networks have good approximation capabilities and inherent adaptivity features, they provide a powerful tool for identification of systems with unknown nonlinearities (Antsaklis, 1990; Miller . 1990).
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Neural Control Application to Combustion Processes,ations, pressure oscillations are undesirable since they result in excessive vibration, causing high levels of acoustic noise and in extreme cases mechanical failure. In the frequency domain, the pressure is characterised by dominant peaks at discrete frequencies which correspond to the acoustic modes of the combustion chamber.
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1430-9491 control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility st
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Nonlinear Predictive Neural Control,linearities and a linear model may be acceptable only when the process is operating around an equilibrium point. If the process is highly nonlinear, a nonlinear model will be necessary to describe the behaviour of the process.
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Book 2001chnology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers
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