基于布谷鸟搜索算法参数优化的组合核极限学习机

针对单核极限学习机在泛化性能上存在一定局限性的问题, 提出将再生核函数与多项式核函数相结合, 建立一种新的组合核极限学习机模型, 使其具有全局核与局部核的优点, 并选择布谷鸟搜索算法对其参数进行优化选择. 仿真实验结果表明, 采用基于再生核的组合核函数作为极限学习机的核函数可行, 在实验数据集的多值分类和回归问题上, 与传统支持向量机及单核极限学习机相比, 该模型具有更好的泛化性能.

Combined Kernel Extreme Learning Machine Based onCuckoo Search Algorithm Parameter Optimization

Aiming at the problem of the limitations of the generalization performance of thesingle kernel extreme learning machine, we proposed to combine the reproducing kernel function with the polynomial kernel function to establish a new combined kernel extreme learning machine model, which had the advantages of global and local kernels, and selected cuckoo search algorithm to optimize its parameters. The simulation results show that it is feasible to use the combined kernelfunction based on the reproducing kernel as the kernel function of extreme learning machine. Compared with traditional support vector machine and single kernel extreme learning machine, the model has better generalizationperformance in multi\|valued classification and regression of experimental datasets.