PARAMETRIC CONTROL OBJECTS IDENTIFICATION WITH DYNAMIC NEURAL NETWORK MODELS
We develop the identification approach and algorithms for the dynamics of control objects with neural network modeling and active experimentation with the models. The resulting model represented with a dynamic neural network models the behavior of the object and lets us find the object’s output, including outputs for periodic test influences. By the resulting complex frequency response, with the method of least squares we find the parameters of the channel’s transfer function.