Prediction of temperature and CO concentration fields based on BPNN in low-temperature coal oxidation

Zhao, Jianqiao; Yang, Deguang; Wu, Jiaxin; Meng, Xiuxia; Xiao, Li; Wu, Guoguang; Miao, Zhenyong; Chu, Ruizhi; Yu, Shi

doi:10.1016/j.tca.2020.178820

Cited by 39 publications

(4 citation statements)

References 36 publications

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“…𝑙 = is a learning rate with a value between 0 and 1; if l is small in value, then there will be a slight change in weight at each iteration, and vice versa.on The value of the ordinary learning, rate is reduced in the learning process [23].…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Evandari

Soeleman

Pramunendar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.

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Section: Back-propagation Neural Networkmentioning

confidence: 99%

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Evandari

Soeleman

Pramunendar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…To simplify the classification, some pre-processing algorithms are used. Here, the widely used BPNN algorithm is adopted for classification training and recognition [ 23 , 24 , 25 ], and the BPNN structure is shown in Figure 7 . The nodes of the BP method are divided into three layers, input layer, hidden layer, and output layer.…”

Section: Recognition Algorithmmentioning

confidence: 99%

A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes

Qian¹,

Chi²,

Dong³

et al. 2022

Sensors

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In order to develop a non-contact and simple gesture recognition technology, a recognition method with a charge induction array of nine electrodes is proposed. Firstly, the principle of signal acquisition based on charge induction is introduced, and the whole system is given. Secondly, the recognition algorithms, including the pre-processing algorithm and back propagation neural network (BPNN) algorithm, are given to recognize three input modes of hand gestures, digital input, direction input and key input, respectively. Finally, experiments of three input modes of hand gestures are carried out, and the recognition accuracy is 97.2%, 94%, and 100% for digital input, direction input, and key input, respectively. The outstanding characteristic of this method is the real-time recognition of three hand gestures in the distance of 2 cm without the need of wearing any device, as well as being low cost and easy to implement.

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“…The output of each node only affects the output of its corresponding node in the next layer. It has been widely used in prediction, evaluation, language recognition, self-applicable control, and other fields [27][28][29]. The BPNN model is a supervised machine learning model that adopts an error-reverse algorithm and causes the output results to converge to the expected value by adjusting the weights and other parameters during training.…”

Section: Bpnnmentioning

confidence: 99%

Prediction Model and Influencing Factors of CO2 Micro/Nanobubble Release Based on ARIMA-BPNN

Wang

Ren

et al. 2022

Agriculture

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The quantitative prediction of CO2 concentration in the growth environment of crops is a key technology for CO2 enrichment applications. The characteristics of micro/nanobubbles in water make CO2 micro/nanobubble water potentially useful for enriching CO2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO2 micro/nanobubbles. In this paper, the factors influencing CO2 release and changes in CO2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO2 concentration and various influencing factors within a time series is proposed to predict the released CO2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO2 micro/nanobubble water in agriculture.

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Prediction of temperature and CO concentration fields based on BPNN in low-temperature coal oxidation

Cited by 39 publications

References 36 publications

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes

Prediction Model and Influencing Factors of CO2 Micro/Nanobubble Release Based on ARIMA-BPNN

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