The Prediction Model of Cotton Yarn Intensity Based on the CNN-BP Neural Network

Hu, Zhenlong; Zhao, Qiang; Wang, Jun

doi:10.1007/s11277-018-5245-0

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…Therefore, it is widely applied in image recognition, face recognition, data prediction, and so forth. 25 In CNN, the convolutional layer is the core, and its function is to extract the characteristics of the input data. The convolutional layer has two important operations: local correlation and sliding window.…”

Section: Cnn Modelmentioning

confidence: 99%

“…Wu et al 24 proposed a CNN–BP prediction model for precipitation prediction, achieving an impressive prediction accuracy rate of 88.4%. Hu et al 25 proposed a machine learning model to predict the yarn strength index by connecting the CNN and BP neural networks. Lu et al 26 proposed a method based on improved CNN–BP to predict multibeam sonar grid data and proved that the method has feasibility, reliability, and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Ma,

Tian,

Sun

et al. 2024

Fatigue Fract Eng Mat Struct

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This study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

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Section: Cnn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Ma,

Tian,

Sun

et al. 2024

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

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“…Learning Machine (ELM) to predict the strength of spun yarn; Zhenlong et al 4 combined convolutional neural network (CNN) and BP neural network, and proposed a CNN-BP neural network to predict yarn strength; Hu et al 5 considers the influence of time on yarn quality and strength, and proposes a yarn strength and quality prediction model based on artificial recurrent neural network (RNN).…”

Section: Proposed a De-elm Algorithm Combining Differential Evolution (De) Algorithm And Extremementioning

confidence: 99%

“…Subsequently, in the period of the rapid development of neural networks, Furferi and Gelli 2 proposed a model based on feedforward back propagation (BP) artificial neural network to predict yarn strength. Mwasiagi 3 proposed a DE-ELM algorithm combining differential evolution (DE) algorithm and Extreme Learning Machine (ELM) to predict the strength of spun yarn; Zhenlong et al 4 combined convolutional neural network (CNN) and BP neural network, and proposed a CNN-BP neural network to predict yarn strength; Hu et al 5 considers the influence of time on yarn quality and strength, and proposes a yarn strength and quality prediction model based on artificial recurrent neural network (RNN).…”

Section: Introductionmentioning

confidence: 99%

Prediction of yarn unevenness based on BMNN

Jiang

Song

Zhang

et al. 2021

Journal of Engineered Fibers and Fabrics

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With the continuous development of deep learning, due to the complexity of the deep neural network structure and the limitation of training time, some scholars have proposed broad learning, the Broad Learning System (BLS). However, BLS currently only verifies that it has excellent effects on some of the network training data sets, and it does not necessarily have excellent effects on some actual data sets. In response to this, this paper uses the effect of BLS in predicting the unevenness of yarn quality in the yarn data set, and proposes a BLS-based multi-layer neural network (MNN) for the problems, which is called Broad Multilayer Neural Network (BMNN).

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“…In recent years, artificial neural network (ANN) technology has been developed to the practical stage and has achieved fruitful results in many fields [13,14]. A neural network is a large-scale information parallel processing system.…”

Section: System Improvementmentioning

confidence: 99%

Station green lighting system based on sensor network

Wang

Qin

2019

J Wireless Com Network

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Aiming at the problems of high energy consumption, chaotic lines, and inconsistent lighting sources in many stations, this paper presents a simple, reliable, and convenient station green lighting system based on sensor network. According to the arrival and departure time of the train, the lighting equipment of the corresponding track is automatically closed, which saves the power resources and human resources, and improves the service life of the lighting equipment. In addition, an intelligent illumination control method based on BP neural network is proposed. The test is carried out in the real environment of the station. The designed system can intelligently adjust the working conditions of the lighting system, save energy to the greatest extent and meet the practical requirements.

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The Prediction Model of Cotton Yarn Intensity Based on the CNN-BP Neural Network

Cited by 12 publications

References 12 publications

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Prediction of yarn unevenness based on BMNN

Station green lighting system based on sensor network

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