果蝇优化算法在区域高程拟合中的应用

针对最小二乘支持向量机拟合法难以选择最优参数的问题,将果蝇优化算法引入最小二乘支持向量机中,构建区域GPS高程拟合模型的方法,利用果蝇优化算法全局寻优能力强、过程简洁、参数少等优点,解决最小二乘支持向量机的参数寻优问题,并通过最小二乘支持向量机来构建高程拟合模型。结果表明,与BP神经网络拟合方法相比,引入果蝇优化算法的最小二乘支持向量机拟合方法具有更高的稳定性,内符合精度比标准最小二乘支持向量机提高了26%。

Regional Height Fitting Based on FOA-LSSVM Precision Analysis

For the problem that the least squares support vector machine fitting method is difficult to select the optimal parameters,the fruit fly optimization algorithm is introduced into the least squares support vector machine to construct the regional GPS height fitting model.As with strong global optimization ability,simple process and few parameters,the fruit fly optimization algorithm is used to solve the parameter optimization problem of the least squares support vector machine,and then the height fitting model is constructed by the least squares support vector machine.The experimental results show that,compared with the BP neural network fitting method,the least squares support vector machine optimized by fruit fly algorithm fitting method has higher stability,and the internal accuracy is also improved by 26%compared with the standard LSSVM.