Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (08): 121-127.doi: 10.13475/j.fzxb.20190402507

• Machinery & Accessories • Previous Articles     Next Articles

Prediction of loom efficiency based on BP neural network and its improved algorithm

ZHANG Xiaoxia1, LIU Fengkun2, MAI Wei1, MA Chongqi1()   

  1. 1. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China
    2. China Textile Information Center, Beijing 100020, China
  • Received:2019-04-08 Revised:2020-05-09 Online:2020-08-15 Published:2020-08-21
  • Contact: MA Chongqi E-mail:tjmcq@tiangong.edu.cn

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Abstract:

In order to predict the loom efficiency more accurately in the weaving workshop of textile mills, three models, i.e. BP neural network, principal component analysis combined with BP neural network(PCA-BP) and genetic algorithm modified BP neural network model (GA-BP), were used to predict the loom efficiency. At the same time, the prediction results of the GA-BP were compared with that of the BP neural network and PCA-BP neural network. The results show that the GA-BP has the best fitting degree to the original data, the correlation coefficient is 0.946 87, which is 6.42% higher than BP and 2.61% higher than PCA-BP. The average absolute errors between the simulated output value and the expected loom stoppage values over 100 000 weft insertions are 0.341 2, 0.303 1 and 0.234 1, respectively, for GA-BP, PCA-BP and BP models, corresponding to error percentages 8.63%, 7.67% and 5.92%. The average errors between the predicted and the expected values of the loom efficiency with different network models are 3.010 9, 2.688 4 and 2.118 9, respectively, with error percentages of 3.51%, 3.13%, 2.47%. The order of prediction accuracy of the three models is GA-BP, PCA-BP and BP.

Key words: BP neural network, genetic algorithm, principal component analysis, prediction model, loom efficiency prediction

CLC Number: 

  • TS104.2

Fig.1

Network topology"

Fig.2

R corresponding to number of nodes in different hidden layers"

Fig.3

Improvement of BP neural network flow chart by genetic algorithms"

Fig.4

GA-BP neural network output values of training (a), validation(b), testing(c) and overall(d) data"

Fig.5

Three network simulation output values. (a)BP neural network output; (b)PCA-BP neural network output; (c) Sufficiency curve of GA-BP"

Tab.1

Error between simulation value and expected value of 100 000 weft warp stop in different networks"

验证样本
序号		 期望值 BP PCA-BP GA-BP
仿真值1 误差1 仿真值2 误差2 仿真值3 误差3
1 4.42 4.384 2 0.035 8 3.976 4 0.443 6 4.412 0 0.008 0
2 5.91 5.323 3 0.586 7 6.180 8 0.270 8 5.206 6 0.703 4
3 6.61 6.337 0 0.273 0 6.351 8 0.258 2 6.421 4 0.188 6
4 1.81 2.042 9 0.232 9 1.674 5 0.135 5 1.807 8 0.002 2
5 2.45 1.646 8 0.803 2 2.588 0 0.138 0 2.364 8 0.085 2
6 3.96 3.522 1 0.437 9 3.516 5 0.443 5 4.371 6 0.411 6
7 1.10 1.593 8 0.493 8 1.610 3 0.510 3 1.358 3 0.258 3
8 1.69 1.857 7 0.167 7 2.271 7 0.581 7 1.751 8 0.061 8
9 1.98 1.857 7 0.122 3 2.271 7 0.291 7 1.751 8 0.228 2
10 6.77 5.882 2 0.887 8 6.877 0 0.107 0 6.194 4 0.575 6
11 6.61 6.717 6 0.107 6 6.685 6 0.075 6 6.415 1 0.194 9
12 2.73 2.639 4 0.090 6 2.167 6 0.562 4 2.437 4 0.292 6
13 1.64 1.917 9 0.277 9 1.567 4 0.072 6 1.878 3 0.238 3
14 3.45 3.095 9 0.354 1 3.479 5 0.029 5 3.256 9 0.193 1
15 5.52 5.406 7 0.113 3 5.124 4 0.395 6 5.507 0 0.013 0
16 5.28 5.045 1 0.234 9 5.490 0 0.210 0 5.275 6 0.004 4
17 5.19 5.454 3 0.264 3 4.745 1 0.444 9 5.175 2 0.014 8
18 4.00 3.342 9 0.657 1 4.484 6 0.484 6 3.260 5 0.739 5
平均误差 0.341 2 0.303 1 0.234 1
误差百分率/% 8.63 7.67 5.92

Tab.2

Error between simulation value and expected value of loom efficiency in different networks"

验证样本
序号		 期望值 BP PCA-BP GA-BP
仿真值1 误差1 仿真值2 误差2 仿真值3 误差3
1 91.93 88.504 0 3.426 0 90.006 8 1.923 2 94.073 9 2.143 9
2 79.48 76.990 0 2.490 0 85.418 5 5.938 5 76.359 6 3.120 4
3 76.38 77.141 8 0.761 8 77.747 7 1.367 7 75.495 9 0.884 1
4 93.41 88.099 5 5.310 5 85.948 9 7.461 1 92.445 8 0.964 2
5 81.02 88.169 2 7.149 2 79.229 8 1.790 2 85.757 5 4.737 5
6 78.88 77.702 4 1.177 6 79.547 8 0.667 8 80.209 5 1.329 5
7 89.90 87.943 2 1.956 8 89.116 3 0.783 7 90.230 6 0.330 6
8 85.83 88.259 7 2.429 7 86.305 4 0.475 4 88.629 8 2.799 8
9 91.29 88.259 7 3.030 3 86.305 4 4.984 6 88.629 8 2.660 2
10 77.47 81.749 9 4.279 9 79.337 2 1.867 2 80.084 9 2.614 9
11 91.63 88.011 9 3.618 1 88.622 6 3.007 4 90.704 6 0.925 4
12 88.17 88.619 3 0.449 3 86.600 5 1.569 5 89.649 2 1.479 2
13 88.81 88.349 7 0.460 3 85.620 3 3.189 7 88.014 9 0.795 1
14 87.57 82.818 7 4.751 3 86.269 0 1.301 0 82.425 0 5.145 0
15 82.33 77.342 4 4.987 6 85.298 4 2.968 4 83.573 5 1.243 5
16 87.01 86.629 4 0.380 6 86.856 1 0.153 9 87.300 5 0.290 5
17 88.11 82.447 3 5.662 7 89.289 8 1.179 8 92.079 7 3.969 7
18 84.48 82.606 1 1.873 9 92.242 3 7.762 3 81.772 6 2.707 4
平均误差 3.010 9 2.688 4 2.118 9
误差百分率/% 3.51 3.13 2.47
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