A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.
复制标题
基于多核方法的电子鼻应用新型极限学习机分类模型
DOI：
10.3390/s17061434
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发表时间：
2017-06-19
期刊：
影响因子：
--
通讯作者：
Xie Q
中科院分区：
文献类型：
--
作者：
Jian Y;Huang D;Yan J;Lu K;Huang Y;Wen T;Zeng T;Zhong S;Xie Q
提出了一种新的分类模型--基于量子行为粒子群优化(QPSO)的加权多核极端学习机(QWMK-ELM)。使用两个不同的电子鼻数据集进行了实验验证。与现有的多核极端学习机(MK-ELM)算法不同，该算法将基核的组合系数作为单隐层前向神经网络(SLFN)的外部参数。利用QPSO算法同时优化基核的组合系数、每个基核的模型参数和正则化参数，然后用复合核函数实现核极值学习机。利用四种常见的单核函数(高斯核、多项式核、Sigmoid核和小波核)来构造不同的复合核函数。此外，该方法还与现有的其他分类方法：极限学习机(ELM)、核极限学习机(KELM)、k近邻(KNN)、支持向量机(SVM)、多层感知器(MLP)、径向基函数神经网络(RBFNN)和概率神经网络(PNN)进行了比较。结果表明，所提出的QWMK-ELM方法不仅在精度上优于上述方法，而且在气体分类效率方面也优于上述方法。
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.