The paper presents iterated algorithm for parameter estimation of non-linear regression model. The non-linear model is firstly approximated by a polynomial. Afterwards, parameter estimation based on measured data is taken as the initial value for the proposed iterated algorithm. As the estimation method, the well-known Least Square Estimation (LSE), artificial neural networks (ANN) or Bayesian methodology (BM) can be used. With respect to the knowledge of initial parameters the measured data are transformed to meet best the non-linear regression criteria (orthogonal data projection). The original and transformed data are used in the next step of the designed iterated algorithm to receive better parameter estimation. The iteration is repeated until the algorithm converges into a final result. The proposed methodology can be applied on all non-linear models that could be approximated by a polynomial function. The illustrative examples show the convergence of the designed iterated algorithm.
The article presents a new rnethodology concerning the GPS signals
Processing and shows the signál pre-processing the influence on the quality of the prediction error. The next paraineter, which qualified the model quality, is exponential forgetting. For slowly tinie dependent models the exponential forgetting is approximately 0.98 - 0.99. The lower forgetting value points out the time varying model which is not usable for our modelling application. At the end of the article we achieved model for GPS signals with the appropriate prediction errors and with adequate exponential forgetting. AU theoretical results are practically applied on reál GPS signals and the achieved accuracy is much better according to the raw measured data.