自适应遗传神经网络模型(IAGA-BP)及其在坝体结构损伤识别中的应用

针对基本遗传算法(SGA)收敛速度慢、计算稳定性差、效率低下和易陷入局部收敛等问题提出了一种改进的自适应交叉和变异算子,采用十进制编码并建立改进的自适应遗传神经网络模型(IAGA-BP),同时建立基本遗传神经网络模型(SGA-BP)。将两者同时运用于坝体结构损伤识别,分析结果表明:IAGA-BP模型在收敛速度、精度方面明显优于SGA-BP模型。

Improved adaptive genetic neural networks model and its application in identification of dam damage

As the simple genetic algorithm has the shortcomings such as low convergence velocity and efficiency,and easily falling into premature convergence,a kind of improved adaptive crossover operator and mutation operator was introduced.Together with a simple genetic neural networks model（SGA-BP） being built,an improved adaptive genetic neural networks model（IAGA-BP） was built using a decimal encoding scheme.By comparison of the analysis results between the two models,it showed that the SGA-BP model was more excellent in the convergence velocity and the precision.