Output Models

Uncertainty analysis model Since the model system, only seeks to show the reality, then inevitably the existence of simplifications, assumptions and idealizations of complex processes and phenomena occurring in the system. The consequence of these simplifications and idealizations are uncertainties in the final results obtained in the application of the model. Uncertainties due to incompleteness of the models are due to that, when constructing models of systems analyst made no provision for some aspects of the simulated processes occurring in the system. The second type of uncertainty associated with the inadequacy of the models. Even in those cases when the model takes into account all the features of the existence and development of systems, the sequence of events, and logical features of the functioning systems incorporated into the model did not accurately reflect reality.

The third type uncertainty – the uncertainty of initial parameters. Parameters of different models are uncertain. The reason for this is lack of data used in the statistical estimation of input parameters inability to accurately describe the behavior of staff working within the system being analyzed, the presence of the assumptions made in the model. Sensitivity analysis of model sensitivity analysis model known procedure for evaluating the influence of tolerances of input parameters on its output characteristics. Conduct sensitivity analysis as follows: define the deviation input to the right and left side of his middle values and fixed, as in this case changes the output value of the model. As the deviation usually take the standard deviation. Set realistic realistic model therefore, answer the question: whether the model is so special cases for which there are already evidence. Efficiency goal of efficiency analysis model to determine how the model is practical and easy to operation. First, the model should provide results within a reasonable time. Second, labor costs and resources required for the operation of the model must fit within the limits of computer time and the fund salary. Must satisfy the condition of practicality. The next aspect of model validation involves analyzing the assumptions and the assumptions made in constructing the model. At this stage, verify that the evaluate the quality model and its properties in terms of real impact of external disturbances and parameters.