## 3 An Improved Effectiveness Evaluation Method Based on Factor Analysis
### Simplified Algorithm Model  
By the analysis of the importance and correlation of the indexes, the evaluation indexes of operational effectiveness of mobile communication system can be simplified as \(X1\), \(X3\), \(X7\) and \(X8\), and the comprehensive weight of these four indexes needs to be adjusted to \(W_{i}^{*}\):  
\[W_{i}^{*}=\frac{W_{i}}{\sum\limits_{j=1}^{k}W_{j}}\ \ \ i=1,2,\cdots,k \tag{15}\]  
\(W_{i}^{*}\) is the new normalized commonness of the index \(i\), \(W_{i}\) is the original normalized commonness of the index \(i\) and \(k\) is the number of simplified indexes.  
According to the weight of each index and the standardized data set, the weighted sum can obtain the total score of each unit.  
\[Score_{i}^{*}=\sum\limits_{j=1}^{k}W_{i}^{*}.Z_{ij}\ \ \ i=1,2,\cdots,n \tag{16}\]  
\(Score_{i}^{*}\) is the simplified index score of each unit, \(Z_{ij}\) is the normalized data of each unit and \(n\) is the number of units.  
By calculating the score values of each unit and arranging them by the simplified algorithm model, the comparison with the ranking results of factor comprehensive scores in Table 2 is shown in Table 8.  
Table 8 shows that the maximum deviation between the evaluation ranking of the simplified algorithm model and the ranking results obtained by the previous factor analysis method is 3, in which 42% (13 units) of the evaluation ranking possessed no change, 39% (12 units) of the evaluation ranking possessed one difference, 13% (4 units) of the evaluation ranking possessed two differences and 6% (2 units) of the evaluation ranking possessed three differences. This shows that the evaluation conclusion of the simplified algorithm model is reliable and ideal. In the first stage, the method can be used to evaluate historical data and to calculate simplified algorithmic models. In the second stage, the simplified indexes are used to evaluate the simulation data of the design scheme, which greatly reduces the workload of data recording, statistics and calculation, greatly facilitates the implementation of the late evaluation and has strong economic benefits.  
This method can be applied to the semi-automatic network planning assistant optimization system of mobile communication network of army division/brigade troops. The latest simplified indexes and weights can be obtained by using the historical data and stored in database.  
When network planning needs to be performed again, the network plan set generated by the planning equipment is imported into the system, and the system simulates all the network plans. It only requires a small amount of simulation data to be collected according to the simplified index and can directly calculate the effectiveness of each network plan according to the simplified model.  
After each network operation, data will be imported into the system through the network management device so as to update the indexes and weights and to keep the system updated iteratively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Unit** & **Factor Total Score** & **Factor Ranking** & **Simplified Index Score** & **Simplify Sorting** & **Sort Difference** \\ \hline
1 & 1.644 & 1 & 2.1178 & 1 & 0 \\
2 & 0.582 & 5 & 0.8886 & 5 & 0 \\
3 & \(-\)0.339 & 22 & \(-\)0.3680 & 20 & \(-\)2 \\
4 & \(-\)0.437 &