Abstract
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.
Highlights
An electronic nose (e-nose) is a machine devoted to reproducing the smell processing procedure of the mammalian olfactory system, which has played an immensely crucial role in a wide range of realms, such as disease diagnosis [1], food industry [2], agriculture [3], environmental monitoring and protection [4], etc
In view of the complexity and especially the efficiency in our previous publication [38], quantum-behaved particle swarm optimization (QPSO) [55,56] is leveraged to optimize the values of C in Equation (8), λ1, λ2 in Equation (12), and the model parameters of the base kernels to constitute a weighted multiple kernel and implement the KELM shown in Equation (8), which is named QPSO-based weighted multiple kernel extreme learning machine (QWMK-ELM)
We explored a new framework to enhance the performance of ELM, which was
Summary
An electronic nose (e-nose) is a machine devoted to reproducing the smell processing procedure of the mammalian olfactory system, which has played an immensely crucial role in a wide range of realms, such as disease diagnosis [1], food industry [2], agriculture [3], environmental monitoring and protection [4], etc. The issues of how to effectively capture the “fingerprint” of the smell and how to classify the chemical volatile are possibly the most attractive research on e-noses among the scientific community and have generated enormous interest worldwide [7,8,9] This has been implemented by analyzing the response of the gas sensor array (providing multivariate signals) when exposed to chemical stimuli under well-controlled conditions (i.e., temperature, humidity, exposure time, etc.) [10]. We introduce a simple but effective quantum-behaved particle swarm optimization (QPSO)-based weighed multiple kernel extreme learning machine (QWMK-ELM) model and apply it to e-nose data classification for the first time. This technique is attractive because it is simple and effective, it is promising for future use in online applications.
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