1
|
Yang J, Hu X, Feng L, Liu Z, Murtazt A, Qin W, Zhou M, Liu J, Bi Y, Qian J, Zhang W. AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture. ACS Sens 2024; 9:2925-2934. [PMID: 38836922 DOI: 10.1021/acssensors.4c00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.
Collapse
Affiliation(s)
- Jilei Yang
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Xuefeng Hu
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Lihang Feng
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210009, China
- Anhui Six-Dimensional Sensor Technology Ltd., Fuyang, Anhui 232100, China
| | - Zhiyuan Liu
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Adil Murtazt
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
| | - Weiwei Qin
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Ming Zhou
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Jiaming Liu
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Yali Bi
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Jingui Qian
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Wei Zhang
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| |
Collapse
|
2
|
Li Y, Yang K, He Z, Liu Z, Lu J, Zhao D, Zheng J, Qian MC. Can Electronic Nose Replace Human Nose?-An Investigation of E-Nose Sensor Responses to Volatile Compounds in Alcoholic Beverages. ACS OMEGA 2023; 8:16356-16363. [PMID: 37179643 PMCID: PMC10173318 DOI: 10.1021/acsomega.3c01140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
Electronic nose (E-nose) technology is frequently attempted to simulate the human olfactory system to recognize complex odors. Metal oxide semiconductors (MOSs) are E-noses' most popular sensor materials. However, these sensor responses to different scents were poorly understood. This study investigated the characteristic responses of sensors to volatile compounds in a MOS-based E-nose platform, using baijiu as an evaluation system. The results showed that the sensor array had distinctive responses for different volatile compounds, and the response intensities varied depending on the sensors and the volatile compounds. Some sensors had dose-response relationships in a specific concentration range. Among all the volatiles investigated in this study, fatty acid esters had the greatest contribution to the overall sensor response of baijiu. Different aroma types of Chinese baijiu and different brands of strong aroma-type baijiu were successfully classified using the E-nose. This study provided an understanding of detailed MOS sensor response with volatile compounds, which could be further applied to improve the E-nose technology and its practical application in food and beverages.
Collapse
Affiliation(s)
- Yuzhu Li
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Kangzhuo Yang
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Zhanglan He
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Zhipeng Liu
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Jialing Lu
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Dong Zhao
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Jia Zheng
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Michael C. Qian
- Department
of Food Science and Technology, Oregon State
University, Corvallis, Oregon 97331, United States
| |
Collapse
|
3
|
Lower limb motion recognition based on surface electromyography. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
|
4
|
Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization. SENSORS 2022; 22:s22124374. [PMID: 35746156 PMCID: PMC9227216 DOI: 10.3390/s22124374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/06/2022] [Accepted: 05/16/2022] [Indexed: 12/10/2022]
Abstract
The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.
Collapse
|
5
|
Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. SENSORS 2021; 21:s21227620. [PMID: 34833693 PMCID: PMC8619411 DOI: 10.3390/s21227620] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 02/07/2023]
Abstract
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
Collapse
|
6
|
Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery. SENSORS 2019; 20:s20010050. [PMID: 31861804 PMCID: PMC6983139 DOI: 10.3390/s20010050] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/15/2019] [Accepted: 12/18/2019] [Indexed: 11/17/2022]
Abstract
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.
Collapse
|
7
|
Kladsomboon S, Thippakorn C, Seesaard T. Development of Organic-Inorganic Hybrid Optical Gas Sensors for the Non-Invasive Monitoring of Pathogenic Bacteria. SENSORS 2018; 18:s18103189. [PMID: 30241405 PMCID: PMC6210542 DOI: 10.3390/s18103189] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 09/16/2018] [Accepted: 09/18/2018] [Indexed: 11/17/2022]
Abstract
Hybrid optical gas sensors, based on different organic and inorganic materials, are proposed in this paper, with the aim of using them as optical artificial nose systems. Three types of organic and inorganic dyes, namely zinc-porphyrin, manganese-porphyrin, and zinc-phthalocyanine, were used as gas sensing materials to fabricate a thin-film coating on glass substrates. The performance of the gas sensor was enhanced by a thermal treatment process. The optical absorption spectra and morphological structure of the sensing films were confirmed by UV-Vis spectrophotometer and atomic force microscope, respectively. The optical gas sensors were tested with various volatile compounds, such as acetic acid, acetone, ammonia, ethanol, ethyl acetate, and formaldehyde, which are commonly found to be released during the growth of bacteria. These sensors were used to detect and discriminate between the bacterial odors of three pathogenic species (Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa) grown in Luria-Bertani medium. Based on a pattern recognition (PARC) technique, we showed that the proposed hybrid optical gas sensors can discriminate among the three pathogenic bacterial odors and that the volatile organic compound (VOC) odor pattern of each bacterium was dependent on the phase of bacterial growth.
Collapse
Affiliation(s)
- Sumana Kladsomboon
- Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Phutthamonthon, Nakhon Pathom 73170, Thailand.
| | - Chadinee Thippakorn
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Phutthamonthon, Nakhon Pathom 73170, Thailand.
| | - Thara Seesaard
- Department of Physics, Faculty of Science and Technology, Kanchanaburi Rajabhat University, Kanchanaburi 71000, Thailand.
| |
Collapse
|
8
|
An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder. ALGORITHMS 2018. [DOI: 10.3390/a11020017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
9
|
Wen T, Yan J, Huang D, Lu K, Deng C, Zeng T, Yu S, He Z. Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing. SENSORS 2018; 18:s18020388. [PMID: 29382146 PMCID: PMC5855868 DOI: 10.3390/s18020388] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 01/08/2018] [Accepted: 01/26/2018] [Indexed: 12/17/2022]
Abstract
The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.
Collapse
Affiliation(s)
- Tailai Wen
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Jia Yan
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China.
| | - Daoyu Huang
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Kun Lu
- High Tech Department, China International Engineering Consulting Corporation, Beijing 100048, China.
| | - Changjian Deng
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Tanyue Zeng
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Song Yu
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Zhiyi He
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| |
Collapse
|
10
|
Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine. SENSORS 2018; 18:s18010173. [PMID: 29320453 PMCID: PMC5796358 DOI: 10.3390/s18010173] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 12/30/2017] [Accepted: 01/08/2018] [Indexed: 01/25/2023]
Abstract
A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.
Collapse
|
11
|
Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques. SENSORS 2017; 17:s17112443. [PMID: 29068431 PMCID: PMC5713026 DOI: 10.3390/s17112443] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/10/2017] [Accepted: 10/13/2017] [Indexed: 11/17/2022]
Abstract
Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.
Collapse
|
12
|
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach. SENSORS 2017. [PMID: 28629202 PMCID: PMC5492859 DOI: 10.3390/s17061434] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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.
Collapse
|