Prediction of yarn unevenness based on BMNN

Jiang, Hao; Song, Jiuxiang; Zhang, Baowei; Zhao, Shuai; Wang, Yonghua

doi:10.1177/15589250211037978

Cited by 5 publications

(4 citation statements)

References 18 publications

(23 reference statements)

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“…H. Ghanmi et al proposed a fuzzy artificial neural network prediction model incorporating expert experience and high accuracy by combining artificial neural networks with fuzzy experts [12]. H. Jiang et al) arranged a Broad Multilayer Neural Network for predicting yarn unevenness by combining a comprehensive learning system with a multi-layer neural network [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Yarn Quality Based on Actual Production

Zhang¹,

Zhang²,

Xu³

2023

Journal of Internet Technology

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<p>In recent decades, the neural network approach to predicting yarn quality indicators has been recognized for its high accuracy. Although using neural networks to predict yarn quality indicators has a high accuracy advantage, its relationship understanding between each input parameter and yarn quality indicators may need to be corrected, i.e., increasing the raw cotton strength, the final yarn strength remains the same or decreases. Although this is normal for prediction algorithms, actual production need is more of a trend for individual parameter changes to predict a correct yarn, i.e., raw cotton strength increase should correspond to yarn strength increase. This study proposes a yarn quality prediction method based on actual production by combining nearest neighbor, particle swarm optimization, and expert experience to address the problem. We Use expert experience to determine the upper and lower limits of parameter weights, the particle swarm optimization finds the optimal weights, and then the nearest neighbor algorithm is used to calculate the predicted values of yarn indexes. Finally, the current problems and the rationality of the method proposed in this paper are verified by experiments.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

Prediction of Yarn Quality Based on Actual Production

Zhang¹,

Zhang²,

Xu³

2023

Journal of Internet Technology

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“…Singh et al 8 researched the effect of several key process parameters including: spring stiffness, conveying speed, and coil position on yarn unevenness and showed that the position of the coil and the spring stiffness have a significant effect on the unevenness. Based on the idea of feature nodes and enhancement nodes, Jiang et al 9 proposed a method for yarn unevenness prediction based on broad multilayer neural networks.…”

Section: Introductionmentioning

confidence: 99%

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

Jiang

Song

Zhang

et al. 2022

Journal of Engineered Fibers and Fabrics

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Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed; the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed; the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.

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“…The first category emphasizes the fine-tuning of the input variables with the aim of improving the final prediction result of the models. In this context, techniques such as grey superior analysis, 11 principal component analysis, 12,13 and analysis of variance, 14 often in conjunction with expert insights are employed to determine the most pertinent input variables.…”

mentioning

confidence: 99%

“…This approach seeks to amalgamate the strengths of multiple algorithms, thereby optimizing prediction efficacy. For example, the combination of neural networks and broad learning systems, 14 neural networks integrated with fuzzy logic, 18 adaptive neuro-fuzzy inference systems in conjunction with subtractive clustering, 19 the melding of convolutional neural networks and generalized regression neural networks, 20 and so on.…”

mentioning

confidence: 99%

A two-layer stacking regression model for predicting yarn quality of small samples

Liu,

Chen,

Wang

2024

Textile Research Journal

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In addressing the prevalent challenges associated with the limited generalization ability and weak adaptability to small samples observed in neural network models in contemporary yarn quality prediction research, this study proposes a two-layer stacking regression model. The initial layer of the stacking model integrates three base models: random forest, gradient boosting decision tree, and adaptive boosting. The secondary layer employs the linear regression algorithm. The efficacy of the stacking model was juxtaposed against the performance assessment of a multilayer perceptron neural network model, in addition to evaluations of the single random forest, gradient boosting decision tree, and adaptive boosting models, respectively. The generalization ability of the models was assessed through experiments that randomly partitioned the sample dataset based on 10 distinct random seeds. The adaptability to small samples of the models was evaluated through experiments spanning nine varying training set sample sizes. The experiment results underscore that the stacking model outperforms in aspects of prediction accuracy, generalization ability, and adaptability to small samples.

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Prediction of yarn unevenness based on BMNN

Cited by 5 publications

References 18 publications

Prediction of Yarn Quality Based on Actual Production

Prediction of Yarn Quality Based on Actual Production

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

A two-layer stacking regression model for predicting yarn quality of small samples

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