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Design of Plant Estimator Model Using Neural Network


K.Suresh Manic, R.Sivakumar, V.Nerthiga, R.Akila, K.Balu


Vol. 9  No. 6  pp. 142-147


The construction of a parameter (or state) estimator can be basically considered as a function approximation problem. To design an estimator, it is first necessary, to obtain the training data set ‘G’ such that, this training data set contains as much information as possible about a system ‘g’. Once trained properly, the estimator will adaptively follow the slope of ‘g’ at all times. In this paper, signals are processed in real time and combined with previous monitoring data to estimate, using the neural network, the process variable level in a nonlinear process control plant.


Estimator, Neural Network, Nonlinear control, Sensor validation