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PREDICTING YARN TENSILE STRENGTH USING ELMAN NETWORK Josphat Igadwa Mwasiagi, Huang XiuBao and Wang XinHou Department of Textile Engineering, Donghua University, Shanghai 1 Introduction The influence of fiber properties to the yarn strength characteristics has been a subject of study by many researchers, [Jackowski et al, 2002; Ureyen et al, 2006, Ghosh et al, 2005(a); Ghosh et al, 2005(b)] The reported artificial Neural Networks (NN) models use HVI characteristics together with yarn fineness and TPI as inputs The reported models have different models for rotor and ringframe yarns This paper discusses the design of a single NN model to predict the tensile strength of rotor and ring spun yarns 2 Elman Network -The Elman network is a type of a recurrent feed forward neural network, with a feedback connection from the output of the hidden layer neurons to the input of the network, -The Elman network has tansig neurons in its hidden (recurrent) layer, and purelin neurons in its output layer 3 The architecture of Elman Network 4 Training Algorithm Training involves adjusting the weights and biases of the network so as to minimize the network’s performance function This can be done using a by using a technique called backpropagation (BP), which involves performing computations backwards through the network [Ham et al, 2003] 5 Fletcher-Reeves Update method - Fletcher-Reeves Update is a modification of the BP technique -it is much faster than the original BP technique 6 Materials Cotton lint and yarn samples were collected from four textile factories in Kenya. For every yarn sample collected, a sample of the corresponding cotton lint mixture used to spin the yarn was also collected The details of the cotton and yarn samples collected are given in table 1. A total of 410 samples were collected. The quality characteristics of the cotton lint and yarn samples were measured under standards laboratory conditions in Shanghai-China 7 Table 1: Cotton Lint and Yarn samples Cotton Lint Mill Code Meru AR Meru AR Meru AR Voi AR Voi AR WT AR Kitui AR Kitui AR Kitui AR D D D B B A A A C Machine Type Yarn Ne Rotor Rotor Rotor Ringframe Ringframe Ringframe Ringframe Ringframe Ringframe 27 12.5 7.5 30 20 30 30 24 24 Spinning Speed (rpm) 68,0000 68,0000 57,0000 11,0000 10, 000 12,000 12,0000 11,0000 8,000 8 Methods A strength prediction algorithm was design as shown below. 9 Methods The NN model used Elman network with FletcherReeves Update as the BP network training algorithm and gradient descent with momentum as the weight/bias learning function was designed. Several options for the number of neurons in the hidden layer were tried, in order to arrive at an optimum design During training, mean square error (mse), which is the average squared error between the networks and the targeted outputs, was used as the performance function To investigate the performance of the network in more details, a regression analysis between the network’s response and the corresponding targets was performed. 10 Results and Discussions 0.2 mse 0.15 0.1 0.05 0 2 4 6 8 10 12 14 16 18 20 22 No. of Neurons Fig. 2. The performance of the Network. 11 Results and Discussions R-Value 1 0.95 0.9 0.85 2 4 6 8 10 12 14 16 18 20 22 No. of Neurons Fig. 3. Prediction ability of the Network. 12 Results and Discussions From figures 2 and 3 the network stabilizes at 6 neurons. Increase of the number of neurons above 6 does not cause any significant change in the performance or prediction ability of the network. 13 Results and Discussions The performance of the trained network for the training, validation and test subsets are given in figure 4. Figure 5 shows the linear regression between the network outputs and the corresponding targets. 14 Results and Discussions 15 Results and Discussions The final mse value for the test data was 0.0156. The network outputs tracks targets reasonable well, with a correlation coefficient (R-value) of 0.974. 16 Conclusions An Elman Network model was trained using Fletcher-Reeves Update conjugate gradient training algorithm. The network predicted the tensile strength of cotton yarn samples consisting of ring and rotor spun yarns, giving an mse value of 0.0156. The correlation coefficient (R-Value) between predicted and targeted values for the network was 0.974. 17 Thank You for Your Kind Attention Q and A 18