基于改进遗传算法优化BP神经网络的糖尿病并发症预测模型#br#

BP神经网络是在深度学习的研究中使用较为频繁的神经网络。本文提出一种改进遗传算法优化BP神经网络的算法（IGABP），利用遗传算法的全局搜索能力优化BP神经网络的初始结构。由于遗传算法易陷入局部最优解，影响自身的寻优能力，故对遗传算法进行改进，最后构建糖尿病并发症预测模型进而预测糖尿病并发症的发生。本文改进遗传算法的选择算子并改进自适应遗传算法的交叉及变异概率公式。通过构建预测模型，将改进后的IGABP与BP、GABP、AGABP进行比较。仿真实验结果表明，使用IGABP进行预测的准确率要明显优于BP、GABP与AGABP，并且加快了网络的收敛速度。

Prediction Model of Diabetic ComplicationsBased on BP Neural Network Optimized by Improved Genetic Algorithm#br#

BP neural network is one of the most frequently used neural networks in deep learning research. In this paper, an improved genetic algorithm （IGABP） is proposed to optimize the initial structure of BP neural network. The genetic algorithm is easy to fall into local optimal solution, which affects its own optimization ability, so the genetic algorithm is improved, and finally the prediction model of diabetes complications is constructed to predict the occurrence of diabetes complications. The selection operator of genetic algorithm is improved, and the crossover and mutation probability formula of adaptive genetic algorithm is improved also. By building a prediction model, the improved IGABP is compared with BP, GABP and AGABP. The simulation results show that the prediction accuracy of IGABP is significantly better than that of BP, GABP and AGABP, and the convergence speed of the network is accelerated.