## 4 Closing Remarks  
This study identified the effect of various learning parameters such as the range of SSP, the number of training data, and the number of hidden layers on performances of RBFNN meta-model with respect to the design approximation. The rotor blade design problems were considered to review the characteristics of RBFNN approximation according to the variation of the learning parameters. The RBFNN based meta-models were generated for the rotor blade design as the actual design problem. In the actual design problem of rotor blade with a number of performance functions, both the error analysis and the statistical analysis were performed. In the error analysis, two accuracy measures (RMSE and MAX) were used to evaluate the performance of RBFNN. With 500 training data and 90 testing data, RBFNN based meta-models of the rotor blade were fitted for each design performance function. From the statistical analysis results of the rotor design problem, it was found that the design performances of rotor blade were increased as the D-spar thickness was increased and the web location was decreased. In the results of the statistical analysis, it was also confirmed that the most sensitive design variable on both the mass and the flap-wise bending stiffness was the D-spar thickness while the most sensitive design variable on the torsional and chord-wise bending stiffnesses was the web location. In regard of the learning parameters variation, first the SSP values were varied from zero to 0.1 with 0.025 step size, secondly the initial number of 500 training data was varied up to 200 training data with 100 data step size, and finally the RBFNN architecture was composed of one layer, two layers, and three layers respectively. From the results of the error analysis on the rotor design problem, it was found that the number of training data had larger influence on the RBFNN meta-model accuracy than the SSP variation while the number of hidden layers had little effect on the performances of RBFNN meta-model. Although the influence of SSP was not significant in the RBFNN approximation of the actual rotor blade design problem, it could be seen that the accuracy of the approximation was increased based on RMSE as the SSP was applied in the range of 0.075 to 0.1. From the results of the number of training data in the rotor blade design approximation, it was able to conclude that the decrement of less than 100 times of number of design variables for the number of training data showed the inferior accuracy due to insufficient learning data for the RBFNN meta-model. It was also found that RMSEs for all the design performance functions were enhanced as the number of training data was increased in the rotor blade design approximation. Regarding to the number of hidden layers, it was found that the influence of the number of hidden layers on the accuracy of the meta-model in the rotor blade design was scarcely detected comparing with the SSP and the number of training data. Although the effect of the number of hidden layers on the accuracy of the meta-model was insignificant, the RMSEs for all the design performance functions tended to slightly improve as the number of hidden layers was increased. This study results can help to suggest a reference index that can reasonably apply the learning parameter value in case of adopting the RBFNN to the design approximation. Even though the RBFNN meta-model was utilized for the design optimizations and the reliability analyses, the role of learning parameters in the RBFNN meta-model was hardly investigated in the design approximation. In most previous studies, the values of parameters in RBFNN meta-model have been applied empirically or adjusted through simple estimation of the accuracy of RBFNN meta-model or comparison with the results of other meta-models. From this study results, the role of the important learning parameters such as the SSP, the number of training data, and the number of hidden layers was identified quantitatively using the rotor blade design problem.