Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model

Peng, Chao; Jia, Yan; Duan, Shukai; Wang, Lidan; Jia, Pengfei; Zhang, Songlin

doi:10.3390/s16040520

Cited by 24 publications

(16 citation statements)

References 54 publications

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“…In view of the complexity and especially the efficiency in our previous publication [38], QPSO [55,56] is leveraged to optimize the values of C in Equation (8),

λ_{1}, λ_{2}

in Equation (12), and the model parameters of the base kernels to constitute a weighted multiple kernel and then implement the KELM shown in Equation (8), which is named QPSO-based weighted multiple kernel extreme learning machine (QWMK-ELM).…”

Section: Methodsmentioning

confidence: 99%

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

Jian

Huang

Jia

et al. 2017

Sensors

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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.

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“…In view of the complexity and especially the efficiency in our previous publication [38], QPSO [55,56] is leveraged to optimize the values of C in Equation (8),

λ_{1}, λ_{2}

Section: Methodsmentioning

confidence: 99%

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

Jian

Huang

Jia

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Meanwhile, ELM's randomly generated input weights and hidden layer biases can make the algorithm unstable. Chao Peng et al [42] proposed an ELM-based Kernel Extreme Learning Machine (KELM) in combination with kernel functions for VOC detection to solve this problem. It was compared against SVM, KNN, and LDA.…”

Section: Non-linear Classification Methodsmentioning

confidence: 99%

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Faleh¹,

Bedoui²,

Kachouri³

2020

Adv. sci. technol. eng. syst. j.

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With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.

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“…Previous studies have shown that multisource information fusion can more effectively detect the composition of tea [33]. Moreover, the time domain and frequency domain features are more effective in extracting internal quality from the sensor signal array [42]. However, there are still some obstacles to the acquisition of features of cross-category tea.…”

Section: Introductionmentioning

confidence: 99%

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

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Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model

Cited by 24 publications

References 54 publications

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

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

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