Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

Cheng, Pengpeng; Chen, Daoling; Wang, Jianping

doi:10.1515/ijnsns-2020-0068

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…e core of the BP neural network algorithm is to realize the forward propagation of signals and then reverse propagation of errors. It reverses transmission of errors from the hidden layer to the input layer through the dynamic adjustment of weights between neurons [9,10].…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…erefore, the error level of the BP neural network is reduced by adjusting the weight to achieve the desired goal [12]. e partial derivative δ of the error function is calculated to obtain a more specific weight adjustment formula using (10):…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…en the error e corresponding to different samples was calculated with (6), and all the errors were summarized to obtain the total error of the network. e error signal δ of the hidden and output layers is calculated and the weight adjustment values Δw ho and Δw ih are obtained according to (10) and (11), and then the new weight matrix of w ih and who is obtained. Finally, comparing the number of input samples with the size of counter k to determine whether all samples participated in the training [17] and if all samples did not participate in the training, then we return to formula (2) for sample training again and compare the overall error e with the preset error e min .…”

Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

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Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.

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Section: Bp Neural Networkmentioning

confidence: 99%

Section: Bp Neural Networkmentioning

confidence: 99%

Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

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Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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“…Rao et al [18] combined particle swarm optimization (PSO), Hopfield neural network, and BP neural network for equipment fault diagnosis. Cheng et al [19] used BP neural network for damp heat detection. Based on variable input genetic algorithm (GA) and BP neural network, a temperature and humidity monitoring and prediction model is proposed.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Performance Evaluation Model for Higher Education Informatization by Means of PLS‐BP Model

Dong

Dan

et al. 2022

Discrete Dynamics in Nature and Society

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In order to improve the intelligent level of higher education informatization, a novel performance evaluation model of higher education informatization by means of PLS-BP model is proposed. The principles of partial least squares (PLS) method and back propagation (BP) neural network are introduced firstly. The main components of the data are extracted by PLS method, and then combined with BP neural network, the PLS-BP model is constructed. At the end of the article, the PLS-BP evaluation model is simulated and analyzed. From the comparison results, the error between the PLS-BP evaluation model and traditional performance evaluation is relatively small, and the maximum relative error is 2.32%. The comparison results verify the accuracy of the PLS-BP model. This model presented in this paper provides a new way for the performance evaluation of higher education informatization.

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“…In this paper, we establish a single connection between smart wearable devices and mobile terminals through the current relatively mature near-field communication technology (e.g., FC technology), Bluetooth technology, and iBeacon technology [22,23]. A diversified and full-coverage interaction mode between multiple smart wearable devices and mobile terminals is established, and a low-consumption and multifunctional optimized connection scheme is concluded.…”

Section: Multifunctional Clothing Design System and Evaluation Systemmentioning

confidence: 99%

Thermal and Wet Comfort of Clothing in Different Environments Based on Multidimensional Sensor Data Fusion and Intelligent Detection

Hou

2022

Journal of Sensors

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With the improvement of quality of life, people pay more and more attention to the comfort performance of clothing, of which thermal and wet comfort is an important part of evaluating the comfort of clothing, referring to the performance of keeping the human body in a reasonable thermal and wet state. When the human body sweats a lot or is in a highly humid environment, the clothing fabric will be soaked to make people feel wet, which seriously affects the comfort performance of clothing wear, and with the rapid development of sensing technology, the comfort of human clothing can be comprehensively evaluated by a variety of sensing data (clothing pressure, temperature, humidity, and heart rate). Therefore, how to analyze and process these data and establish an objective and accurate evaluation criterion for clothing comfort is a difficult problem and has attracted the attention of many researchers. In this paper, an improved kernel function fuzzy kernel c-means clustering algorithm is used to analyze the pressure at specific points in human activities. Unsupervised clustering analysis was performed for five clustering metrics (mean, pressure range, temperature range, humidity range, and heart rate variability). The clustered samples were learned and discriminated by a support vector machine to determine the comfort level of the clothing. The method can be applied to multi-indicator and multiclassification problems, providing smart clothing researchers with an intelligent, objective, and accurate method for evaluating clothing comfort. Experiments show that the method designed in this paper has good performance experience in terms of mean value, pressure range, temperature range, humidity range, and heart rate variability.

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Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

Cited by 11 publications

References 9 publications

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

[Retracted] Analysis of Performance Evaluation Model for Higher Education Informatization by Means of PLS‐BP Model

Thermal and Wet Comfort of Clothing in Different Environments Based on Multidimensional Sensor Data Fusion and Intelligent Detection

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