LVFOA优化的GRNN在财务预警中的应用

针对企业财务数据复杂、非线性等特点，提出了一种基于混沌变步长果蝇算法（LVFOA）优化广义回归神经网络（GRNN）的财务预警模型.首先引入Logistic混沌映射修正FOA的初始值，然后在最优初始值的基础上修正FOA步长为动态步长，寻找最优Spread值，最后对预测数据进行分析，选取有代表性的指标.改进后的果蝇算法显示了更好的全局优化和快速收敛能力，提高了GRNN的预测精度.仿真结果表明，相对于GRNN模型和FOA-GRNN模型， LVFOA-GRNN模型提高了预警准确率，与财务数据的拟合度更高.

Application of GRNN Optimized by LVFOA in the Financial Warning

Aiming at characteristics of complexity and nonlinear in enterprise financial data, this paper puts forward a financial early warning model based on General Regression Neural Network which is optimized by Logistic chaos mapping Variable step size Fruit Fly Optimization Algorithm. Firstly, the Logistic chaos mapping is used in Fruit Fly Optimization Algorithm to modify the initial value. Secondly, based on optimal initial value, we modify the step size of FOA in order to find the best Spread. Finally, we analyze the forecast data and select representative indicators. LVFOA shows better ability of global optimization and fast convergence, and it improves the prediction accuracy of GRNN. The simulation results show that the warning accuracy of new model is higher than GRNN model and FOA-GRNN model, better fitting the complex financial data.