Essay On Sensitivity Analysis

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1.9.1 Sensitivity analysis of the model Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and its propagation. Ideally, uncertainty and sensitivity analysis should be run in tandem. As the optimization method provides the best set of inputs for optimum output, the input parameters are different at all the times and a superlative situation may not be rewarded. Some of the input factors may have sparing effect on the output on the contrary others have dominating effect. Though this situation is not prevalent in ideal situation, it is strongly recommended to check the most dominating input parameter which will have impact on output. This will strengthen the understanding of the manufacturing firm regarding where control is required. To cater this need sensitivity analysis is carried out using MS frontline solver 12.5 for regression and dimensional analysis. 1.9.2 Univariate Analysis Univariate analysis is one of the methods for analyzing data on a single variable at a time. Univariate analysis explores each variable in the data set, separately. So ultimately this is post optimality method for defining most influential input parameters. It primarily computes differential dy/dx values for all inputs. The value of one of the variable is increased by 1 and change in the output is recorded .It provides the better insight for the interaction between process and variable. In order to decrease the output the most dominating factor is incremented. Sensitivity is checked after every increment. The... ... middle of paper ... ...is built by extending the logical basis of contemporary simulation models. Sometimes these 35 parameters may fail to explain the observed noise while predicting .It simply complies that some vital input must be included. Extraneous sense and effect of some unknown variables may have effect on measurement, which must be the part of randomness. Most promising results are attained using ANN simulation in the present investigation. The relationship between the independent and dependent variables is incarcerated correctly by ANN but may not be inclusive for general understanding. Enigmatic nature of this sensible liaison is tranquil tough. Remaining deterministic modeling methods are straightforward to comprehend. The precision of ANN over other deterministic modeling methods must be honored .The multifarious structure of ANN model is prevail over by its precision.

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