A hybrid method for parameter estimation

In this paper a method is presented for plant model parameter estimation. The method combines the artificial neural networks ability for function approximation with a nonlinear least-squares regression technique using the Levenberg-Marquardt optimization method. This combination intends to overcome problems that arise when artificial neural networks or nonlinear least-squares regression are separately applied to parameter estimation, which is accomplished by means of potentiating each of the methods advantages. The estimation of atracurium effect concentration model parameters is used as a case study to show the efficiency of the proposed method.