Real-time identification of the draft system using neural network

High regularity is important in manufacturing spun yarn. Among controllers used in regularity control, existing PID controller is suitable for linear time-invariant systems but not for the control of sliver draft systems with non-linear characteristic. Thus, the present study designed a knowledge-based fuzzy controller in consideration of disturbance and non-linear characteristic of sliver draft systems. RLS (Recursive Least Squares) was used as an estimation algorithm to formulate a model used in computer simulation for implementing the controller, and real time estimation was made using real data from draft systems to get a model close to actual systems. This research used a estimation model in designing a knowledge-based controller that minimizes the variation of control response to the sliver linear density of the system, and the result of linear density control was used to prove the superiority of the controller in control performance through CV%.

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Real-time estimation and irregularity control of the sliver draft system

Bae

Kim

Chun

et al. 2008

Fibers Polym

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An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

Balcı¹,

Ogulata²,

Şahin³

et al. 2008

Fibers Polym

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This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L * and ΔE colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L * and ΔE, we achieved 97 % of R for both of them. We are able to predict the L * value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of L * and ΔE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L * .

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Optimization of the Dyeing Process and Prediction of Quality Characteristics on Elastic Fiber Blending Fabrics

Kuo

Chang

et al. 2008

Polymer-Plastics Technology and Engineering

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Real-time identification of the draft system using neural network

Cited by 4 publications

References 5 publications

Real-time estimation and irregularity control of the sliver draft system

Real-time estimation and irregularity control of the sliver draft system

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

Optimization of the Dyeing Process and Prediction of Quality Characteristics on Elastic Fiber Blending Fabrics

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