A method for mixed data classification base on RBF-ELM network

Li, Qiude; Xiong, Qingyu; Ji, Shengfen; Yu, Yang; Wu, Chao; Yi, Hualing

doi:10.1016/j.neucom.2020.12.032

Cited by 35 publications

(13 citation statements)

References 33 publications

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“…j represents the position of the calculation element in the output layer, σ j represents the center of the Gaussian function, c j represents the variance of the Gaussian function, and the remaining parameters are the same as the above equations [26]. However, the model still needs to be trained.…”

Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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This study is aimed at improving the utilization efficiency of resources and enhancing the experiments’ effect of various composite membrane research. Firstly, the meaning and preparation process of Metal-Organic Frameworks (MOFs) are discussed. Then, the theoretical knowledge of fusing machine learning and multisensor technology is outlined. Finally, based on the controllable fabrication concept of MOF [UIO- (Universitetet I Oslo-) 66]/ZrAl ceramic composite membranes, a multisensor model incorporating machine learning is designed. The results show that the designed radial sensor backpropagation (RS-BP) fusion multisensor model has the highest error rate of about 0.87. When the number of training is about 100 times, the model’s error rate tends to be stable, and the minimum error rate is about 0.01. Secondly, the maximum adsorption capacity of the composite membrane under the controllable preparation of the model is 800 cm3/g Spanning Tree Protocol (STP). Additionally, the adsorption capacity decreases slowly, and the overall adsorption energy is higher than that of the traditional preparation method. Finally, the catalytic efficiency of membranes prepared by fusing multiple sensors is 90%-97%. The research achieves innovation in technology and improves the feasibility of rational application of MOF (UIO-66)/ZrAl ceramic composite membranes. This study not only provides technical support for the development of machine learning fusion multisensing technology but also contributes to the comprehensive improvement of the resource utilization effect.

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Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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“…The purpose of the interview is to understand the mathematics requirements, what mathematical knowledge is needed in professional courses, and how to make mathematics courses better serve professional courses. In order to respect the privacy of the subjects, the questionnaires and interviews are conducted anonymously [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Chen

2022

Advances in Multimedia

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In order to promote the teaching reform, deepen the professional construction, innovate the talent training mode and course teaching mode, improve teachers’ advanced vocational education teaching design ability and teaching level, and promote the connotation construction of higher vocational colleges, an evaluation method of higher mathematics teaching reform effect based on big data analysis technology is proposed. In the research, the teaching status of advanced mathematics courses and the problems existing in teaching are firstly analyzed and studied. Then, on the basis of comprehensive analysis of various influencing factors of course teaching effect evaluation, a set of two-level index system with a total of 25 indicators is established. The results verify the rationality of these indicator settings. Finally, combined with the real historical data of the teaching evaluation effect of a private college, the validity of the proposed model is verified. The experimental results show that the 530 sample data of the course are submitted to the RBF network for the training, and the network converges after 3385 iterations. Among all 100 test samples, the maximum test error is 0.0513, of which 92 samples are judged correct, 7 samples are judged as excellent, and 3 samples are judged as medium. Therefore, the evaluation accuracy rate of the RBF network is as high as 92%. It is concluded that this method provides a new way to evaluate the teaching effect of the course objectively and impartially.

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“…The RBF neural network is a feedforward neural network [53][54][55] and it is composed of the input layer, hidden layer and output layer [56,57]. RBF is an ideal calculation tool for nonlinear problems (see Figure 6 for details on the RBF structure).…”

Section: Digital Construction Quality Evaluation Model For Asphalt Pavement Based On Ipso-rbf 41 Establishment Of Evaluation Model Based mentioning

confidence: 99%

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Zhang

Philbin

et al. 2021

Buildings

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With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.

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A method for mixed data classification base on RBF-ELM network

Cited by 35 publications

References 33 publications

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

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