# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 3 Simulation Validation
### Comparison and Discussion  
To clarify the efficiency and robustness of the proposed system, a comparison among the three models was necessary (as shown in Tables 2-4 and Figure 7), and some phenomena and deduction are found. First, when noise level \(\varepsilon=1\%\), it can be found that the average IA exceeds 80% for all three models of the system. Furthermore, benefiting from data-fusion processing of redundant and complementary information, the average IAs of both the eigen-level and decision-level models exceed 80% when \(\varepsilon=2\%\), and higher than those of the single PNN model, and the maximum of average IA is as high as 89.9% in the decision-level fusion model. The comparison of identification accuracy also showed that the eigen-level fusion model is the best; on the other hand, the single PNN classifier is the worst. This conclusion confirmed again that, especially when a large structural health monitoring system contains measured data with enormous samples and uncertainties, it is very sensible to choose a fusion model of the proposed system for damage detection, because of its good damage detection capability, noise tolerance and robustness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise** & \multirow{2}{*}{**Classifiers Fusion**} & \multicolumn{3}{c}{**IA of Testing Samples for Each Pattern (\%)**} & \multicolumn{1}{c}{**Total IA**} & \multicolumn{1}{c}{**Average**} \\
**Level** & & **Method** & **Pattern 1** & **Pattern 2** & **Pattern 3** & **Pattern 4** & **(\%)** & **of Total** \\ \cline{3-8} \cline{5-8}  
Second, as shown in Figure 7, it is quite clear that the IA decreases with the increase of noise levels. Nevertheless, by using of data fusion, both eigen-level and decision-level models had better performance than single PNN models. Compared with single PNN models, the increase average IA of the eigen-level fusion model are 5.2%, 9.4% and 6.7% when \(\varepsilon=1\%\), 2% and 3%, respectively. Then, the increase values are 6.4%, 5.3% and 2.1% correspondingly for the decision-level fusion model. This confirmed that both data-fusion models have good performance at different noise levels, even with the influence of noises and a great volume. In summary, the comparison results showed that the proposed hybrid system was effective and suitable for structural damage detection. Third, the performance of single PNN classifiers was improved remarkably in the proposed system by data-fusion. For example, among these three single classifiers, the total IA of PNN Classifier 2 was the lowest, namely, 81.5%, 73.5% and 64.25% when \(\varepsilon=1\%\), 2% and 3%, respectively. Moreover, through the processing of data-fusion, the IA were enhanced for eigen-level fusion models at different noise levels. The same conclusions were also found in the decision-level models. In addition, based on eigen-level and decision-level fusion, the IA values of all four patterns improve remarkably at every noise level, especially for pattern 1. Tables 2-4 showed that the minimum IAs of pattern 1 belong to different models of the proposed system. It can be found that, for pattern 1, the increment of average IA amounts to 4.4%, 7.7% and -7.3% when \(\varepsilon=1\%\), 2% and 3%, respectively, for the eigen-level model; correspondingly, the average IA increment is 2.95%, 7.2% and 9.25%, respectively, for the decision-level model. These facts proved again that the data fusion models were suitable for damage detection, because of its good noise tolerance and robustness. In summary, this novel proposed hybrid system is very effective for damage detection, and the highlight points are its good noise tolerance and robustness. These three intelligent models are specialized in some certain situations.