基于改进GA算法优化RBF网络的航空发动机叶片损伤图像识别

通过对航空发动机叶片损伤图像进行识别，可以快速准确的发现叶片损伤状况，有利于对故障进行及时有效的预测。本文对损伤图像进行分割，提取损伤图像特征参数，采用改进GA算法优化RBF网络参数的方法建立航空发动机叶片损伤图像识别模型，对损伤图像特征参数样本进行仿真实验，识别正确率为93.33%，同时与单一RBF网络模型识别结果进行对比分析，结果表明该方法更加优越有效。

Aero-engine Blades Damage Image Recognition Based on RBF Network Optimized by Improved Genetic Algorithms

Through the pattern recognition of aero-engine blades damage image, the blades damage condition can be fast and accurate detection and it is effective to predict malfunction problems. The damage image is segmented and characteristic parameter is extracted. The adaptive genetic algorithm is used to optimize the basis neural network parameters, and dynamic adaptive GA-RBF recognition model is established. The method is applied in simulation. The classification accuracy rate is 93.33%. The method is compared with the radial basis neural network, the result show this method is more effective than the basis neural network.