Evaluation Of A Performance Evaluation Essay
In order to assess the model accuracy, it is necessary to use some quantitative measures of learning. In this study the Mean Squared Error (MSE) and regression analysis were used to evaluate the model performance. MSE is a useful measure of success for numeric prediction and is calculated using Eq. (1). It is worth mentioning that small values of MSE indicate better performance of the ANN model. It was found that the optimum performance of the model is at 25 neurons with MSE 0.1481. The accuracy of the model with different neurons is shown in Table 5.
MSE=(∑_(j=0)^P▒∑_(i=0)^N▒〖(t_ij-y_ij)〗^2 )/NP (5)
Where P is the number of output possessing elements, N is the number of observations, tij are the target outputs and yij are the actu9al outputs.
TABLE 6 accuracy for the network
Neurons in the Hidden Layers MSE Values
Fig. 11 shows the regression analysis of targets and outputs for Levenberg-Marquardt algorithm during training and testing process. The best fit lines in Fig. 10 demonstrate the relationship between the desired value and actual value. As shown in Fig. 10 the R value of ANN model is 0.8134 which indicates that LM algorithm has high ability to train data for the model. Overall, the network has accuracy of 81%.
Figure 11: Neural Network Regression Analysis
The Proposed Algorithm of Neural Network Based Model for Requirements…