选择性集成核极限学习机建模及其应用研究

Selective Ensemble Kernel Extreme Learning Machine Modelling Approach with It's Application Research

针对选择性集成逆向传播神经网络(GASEN-BPNN)模型训练学习速度慢,选择性集成极限学习机(GASEN-ELM)模型建模精度稳定性差等问题,提出一种基于遗传算法的选择性集成核极限学习机(GASEN-KELM)建模方法。该方法首先通过对训练样本进行随机采样获取子模型训练样本;然后采用泛化性、稳定性较佳的核极限学习机(KELM)算法建立候选子模型,通过标准遗传算法工具箱,依据设定阈值按进化策略优化选择最佳子模型;最后通过简单平均加权集成的方式获得最终GASEN-KELM模型。采用标准混凝土抗压强度数据验证了所提出方法的有效性,并与GASEN-BPNN和GASEN-ELM选择性集成算法进行比较,表明所提出方法可以在模型学习速度和建模预测稳定性方面获得较好的均衡。

The BPNN network based selective ensemble model needs long learning time to complete the training task. Although the ex- treme learning machines （ELM） based selective ensemble model has faster learning time than BP network, the predict results are not always stability. Aiming to solve these problems, a newly genetic algorithm based selective ensemble kernel extreme learning machines （GASEKELM） modelling approach is proposed. At first, the sub-models＇training samples are obtained by random sampling the training data. Then, kernel extreme learning machine （KELM） modelling algorithm with good generalization and stability is used to construct the candidate sub-models. And standard genetic algorithm toolbox is used to select the best sub-models according the pre - set threshold using the evolutionary strategy. Finally, the selective ensemble model is obtained by using simple average weighting on the selected sub- models. Base on the proposed method, the soft sensor model of the concrete compression strength is constructed, which verifies the va- lidity of the proposed method. This approach also compares the proposed method with BPNN network and ELM based selective ensemble learning method. The results show that the GASEKELM approach can obtain a trade-off between the model＇s learning speed and predict stability. The prediction can also be used to the reorganization problem based on the image, radar and photoelectric data, which has a bright future.