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Identification of Linear Dynamical Time-variant Systems using Feedforward Neural Network

By Dr S V Dudul and Dr A A Ghatol


In this paper, the authors have attempted the identification of linear time-varying discrete time dynamic system. It is supposed that such systems are signified by transfer function characterizations. As the behaviour of the system changes, the neuron model developed keeps track of the changes in the characteristics and parameters of the system. Thus, at any instant of time, it correctly simulates the given time-varying system, despite the significant changes in system’s property. The excellent approximating capability of the neural network is used to identify the relationship between system variables and parameters. In essence, a neural network perfectly mimics and identifies the actual physical system. It is shown that a simple feedforward neural network containing a single neuron fairly accurately simulates the linear dynamical time-variant system under consideration, which may have Auto-regressive or Moving-average or Autoregressive moving-average model. Keywords: Dynamical; Time-variant; Auto regressive model; Moving average model; Auto regressive moving average model with exogenous inputs; Adaptive system identification; Neural networ

Year: 2009
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