Abstrakt

Nonlinear System Identification Using Maximum Likelihood Estimation

Siny Paul, Bindu Elias

Different algorithms can be used to train the Neural Network Model for Nonlinear system identification. Here the ‘Maximum Likelihood Estimation’ is implemented for modeling nonlinear systems and the performance is evaluated. Maximum likelihood is a well-established procedure for statistical estimation. In this procedure first formulate a log likelihood function and then optimize it with respect to the parameter vector of the probabilistic model under consideration. Four nonlinear systems are used to validate the performance of the model. Results show that Neural Network with the algorithm of Maximum Likelihood Estimation is a good tool for system identification, when the inputs are not well defined.