Maximum likelihood parameter determination method for complex system modeling

Models are often used to characterize a complex system in analysis problems. In modeling process, it is very difficult that model parameters are determined. So, a maximum likelihood parameter determination method for complex system modeling is presented to solve this problem. Therefore, a merge method is adapted to achieve the model parameter for complex systems. The presented method is a kind of merge way and a statistics mode. The parameter values are obtained by having datum is summarized. In this text, the distribution function of unit life is established according to their probability properties. The expressions of the unit failure probability are gotten respectively. Because electromechanical system lifecycle always follows the Weibull distribution, and there are these limitations of small sample and incomplete data, the exponential distribution function is applied as a special way to determine parameter values. Then an extrapolation mode is adapted for the parameter computing. Finally, an example is explored to illustrate the proposed methods. This result is shown that presented methods are effective and feasible. And this method can be widely applied in model parameter determination for another complex system.