Neural Networks: An Alternative Solution for Statistically Based Parameter Prediction

Desai, J. V.; Kane, C.; Bandyopadhayay, B.

doi:10.1177/004051750407400307

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…These were mean square error (mse), training time and correlation coefficient (R 2 ). These are the factors commonly reported by many researchers (Cheng and Adams 1995;Chattopadhyay et al 2004;Desai et al 2004;Majumdar and Majumdar 2004;Majumdar et al 2005;Huang et al 2006a;Ureyen and Gurkan 2008a, b;Mehment 2009). The ELM trained algorithms were trained in a similar manner like the LMBP algorithm, however the training algorithm used was ELM.…”

Section: Designing and Training Of Prediction Modelsmentioning

confidence: 80%

The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn

Mwasiagi

2016

Fash Text

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The increasing use of artificial neural network in the prediction of yarn quality properties calls for constant improvement of the models. This research work reports the use of a novel training algorithm christened extreme learning machines (ELM) to prediction yarn tensile strength (strength). ELM was compared to the Backpropagation (BP) and a hybrid algorithm composed of differential evolution and ELM and named DE-ELM. The three yarn strength prediction models were trained up to a mean squared error (mse) of 0.001. This is an arbitrary level of mse that was selected to enable a comparative study of the performance of the three algorithms. According to the results obtained in this research work, the BP model needed more time for training, while the ELM model recorded the shortest training time. The DE-ELM model was in between the two models. The correlation coefficient (R2) of the BP model was lower than that of ELM model. In comparison to the other two models the DE-ELM model gave the highest R2 value.

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Section: Designing and Training Of Prediction Modelsmentioning

confidence: 80%

The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn

Mwasiagi

2016

Fash Text

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“…In the field of textiles, artificial neural networks (mostly using back-propagation) have been extensively studied during the last two decades [4][5][6]. In the field of spinning previous research has concentrated on predicting the yarn properties and the spinning process performance using the fiber properties or a combination of fiber properties and machine settings as the input of neural networks [7][8][9][10][11][12]. Back-propagation is a supervised learning technique most frequently used for artificial neural network training.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame

Farooq

Cherif

2008

Textile Research Journal

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The evenness of the yarn plays an increasingly significant role in the textile industry, while the sliver evenness is one of the critical factors when producing quality yarn. The sliver evenness is also the major criteria for the assessment of the operation of the draw frame. In principle, there are two approaches to reduce the sliver irregularities. One is to study the drafting mechanism and recognize the causes for irregularities, so that means may be found to reduce them. The other more valuable approach is to use auto-levelers [1], since in most cases the doubling is inadequate to correct the variations in sliver. The control of sliver irregularities can lower the dependence on card sliver uniformity, ambient conditions, and frame parameters.At the auto-leveler draw frame (RSB-D40) the thickness variations in the fed sliver are continually monitored by a mechanical device (a tongue-groove roll) and subsequently converted into electrical signals. The measured values are transmitted to an electronic memory with a variable, the time delayed response. The time delay allows the draft between the mid-roll and the delivery roll of the draw frame to adjust exactly at that moment when the defective sliver piece, which had been measured by a pair of scanning rollers, finds itself at a point of draft. At this point, a servo motor operates depending upon the amount of variation detected in the sliver piece. The distance that separates the scanning rollers pair and the point of draft is called the zero point of regulation or the leveling action point (LAP) as shown in Figure 1. This leads to the calculated correction on the corresponding defective material [2,3]. In auto-leveling draw frames, especially in the case of a change of fiber material, or batches the machine settings and process controlling parameters must be optimized. The LAP is the most important auto-leveling parameter which is influenced by various parameters such as feeding speed, material, break draft gauge, main draft gauge, feeding tension, break draft, and setting of the sliver guiding rollers etc. 1 Previously, the sliver samples had to be produced with different settings, taken to the laboratory, and examined on Abstract Artificial neural networks with their ability of learning from data have been successfully applied in the textile industry. The leveling action point is one of the important auto-leveling parameters of the drawing frame and strongly influences the quality of the manufactured yarn. This paper reports a method of predicting the leveling action point using artificial neural networks. Various leveling action point affecting variables were selected as inputs for training the artificial neural networks with the aim to optimize the auto-leveling by limiting the leveling action point search range. The Levenberg-Marquardt algorithm is incorporated into the back-propagation to accelerate the training and Bayesian regularization is applied to improve the generalization of the networks. The results obtained are quite promising.

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“…Dr. J. V. Desai et. el [14] implemented yarn property prediction & showed that single neural structure is unable to predict all the yarn properties with desired accuracy. But with the help of powerful algorithms this conclusion can be discarded.…”

Section: Introductionmentioning

confidence: 99%

“…limited number of yarn properties [9,14]. It is more desirable to find out fiber properties in advance according to customer design yarn properties i.e.…”

mentioning

confidence: 99%

Optimization of spinning process using computational intelligence

2016

IJLTET

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Neural Networks: An Alternative Solution for Statistically Based Parameter Prediction

Cited by 18 publications

References 6 publications

The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn

The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn

Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame

Optimization of spinning process using computational intelligence

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