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Risk analysis of production system, while the actual and appropriate data is not available, will cause wrong system parameters prediction and wrong decision making.
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Finally, we investigate numerically targeted energy transfer efficiency in this system over a wide range of system parameters and verify the analytical predictions.
The paper introduces spline wavelets as a modelling tool for system identification and proposes the technique of consistent output prediction using wavelets for estimating system parameters.
The system is first taken off-line, so that the output is not actual behavior but only predictions or anticipations of behavior, and inputs and system parameters are accordingly not limited to those that would regulate one's own behavior.
Product quality prediction would allow a manufacturer to make better choices of system parameters at the early design stage and, hence, enhance competitiveness through achieving higher quality levels.
The model predictions are used to determine how to tune the system parameters, and hence their physical counterparts, in order to change the dynamic behaviour of the network.
Comparison of the predicted value with an actual measurement gives the prediction error, which can be seen as a function of the system parameters.
Next, numerical simulations are carried out to verify our theoretical predictions concerning various dynamic behaviors that can occur under suitable conditions on the system parameters.
System parameters are successfully estimated for both harmonic and step responses using theory and measurements, and the dynamic force measurements are analyzed using analytical predictions.
Table 1 System parameters.
In this paper, the attempts made by the authors to develop an artificial neural network system for prediction of injection moulding process parameters is presented.
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