A method for creating a non-linear, stationary or dynamic overall model of a control variable of a combustion engine or partial systems thereof is based on simplified partial model functions that are used to determine in a weighted fashion at each desired operating point the total output quantities from the partial model function with an associated weighting function. The difference between the total output quantity and the real value is determined for all real operating points; and in areas of operating points with an absolute value of this difference that is above the preset value, a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value.The steps for determining the difference between the total output quantity of the associated partial model functions and a real value of the control value as well as the application of a further model and weighting function are executed as many times as needed until the statistically evaluated prediction quality of the overall model has reached a desired value.
A method for creating a non-linear, stationary or dynamic overall model of a control variable of a combustion engine or partial systems thereof is based on simplified partial model functions that are used to determine in a weighted fashion at each desired operating point the total output quantities from the partial model function with an associated weighting function. The difference between the total output quantity and the real value is determined for all real operating points; and in areas of operating points with an absolute value of this difference that is above the preset value, a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value.The steps for determining the difference between the total output quantity of the associated partial model functions and a real value of the control value as well as the application of a further model and weighting function are executed as many times as needed until the statistically evaluated prediction quality of the overall model has reached a desired value.
A method for creating a non-linear, stationary or dynamic overall model of a control variable of a combustion engine or partial systems thereof, is based on simplified partial model functions that are used to determine in a weighted fashion at each desired operating point the total output quantities from the partial model function with an associated weighting function. The difference between the total output quantity and the real value is determined for all real operating points; and in areas of operating points with an absolute value of this difference that is above the preset value, a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value. To use such a method in order to arrive faster, i.e. with fewer iterations, at the optimal overall model that satisfies a statistically substantiated high level of prediction quality and to create an overall model made up of as few partial models as possible, the steps for determining the difference between the total output quantity of the associated partial model functions and a real value of the control value as well as the application of a further model and weighting function are executed as many times as needed until the statistically evaluated prediction quality of the overall model has reached a desired value.
A method for the automatic optimization of an output quantity of a system that is dependent on a plurality of input quantities (for example, an internal combustion engine) with maintenance of secondary conditions determines a theoretical value for the output quantity and for the secondary conditions on the basis of a respective model function having the input quantities as variables, and thereby respectively modifies, in successive individual steps, one of the input quantities within a variation space having a dimension corresponding to the number of input quantities, whereby values, corresponding to the respective input quantities, for the output quantity and for the secondary conditions are also determined directly at the system and are used for the correction of the model functions, until the model function has achieved its optimal value, satisfying the secondary conditions, for the output quantity. In order to reach an assured optimal value for the system more rapidly and with lower expense, in a first stage the modification of the input quantities for the calculation and determination at the system takes place in an arbitrarily predetermined sequence, whereby for each input quantity an individual predetermined step size is not exceeded, and, after the predetermined sequence has been processed, the combination of input quantities that is closest to the optimal value is used as the starting point for the second stage.
A method for the automatic optimization of an output quantity of a system that is dependent on a plurality of input quantities (for example, an internal combustion engine) with maintenance of secondary conditions determines a theoretical value for the output quantity and for the secondary conditions on the basis of a respective model function having the input quantities as variables, and thereby respectively modifies, in successive individual steps, one of the input quantities within a variation space having a dimension corresponding to the number of input quantities, whereby values, corresponding to the respective input quantities, for the output quantity and for the secondary conditions are also determined directly at the system and are used for the correction of the model functions, until the model function has achieved its optimal value, satisfying the secondary conditions, for the output quantity. In order to reach an assured optimal value for the system more rapidly and with lower expense, in a first stage the modification of the input quantities for the calculation and determination at the system takes place in an arbitrarily predetermined sequence, whereby for each input quantity an individual predetermined step size is not exceeded, and, after the predetermined sequence has been processed, the combination of input quantities that is closest to the optimal value is used as the starting point for the second stage.