1. The Application of Artificial Neural Network in the Classification of Common Woven Fabrics Hongbin Jin Shanghai Customs College

2. 2 Commodity Classification b Fundamental work b Mainly performed by people Time-consumingTime-consuming Easy to make mistakesEasy to make mistakes b Objective: predict the classification of common woven fabrics using Artificial Neural Network (ANN)

3. 3 What is ANN b b An ANN is a mathematical model based on biological neural networks. b b An ANN is characterized by three things: 1. 1.ArchitectureArchitecture 2. 2.Activation functionActivation function 3. 3.Learning algorithm: Back-Propagation (BP)

4. 4 A typical ANN architecture

5. 5 Artificial Neuron Stimulus Response

6. 6 Activation function

7. 7 Methods Woven fabric, weighing 150g/m 2, consisting of 60% cotton and 40% staple fibers of polyester 5210 x1 (Dominant fiber): cotton x2 (Content): 60% x3 (Secondary fiber): polyester x4 (Weight): 150g/m 2 y1 (Chapter): 52 y2 (Order): 10

8. 8 ANN architectures used b One-hidden-layer containing 18 neurons b Two-hidden-layer containing 8+8 neurons b Two-hidden-layer containing 14+8 neurons

9. 9 Results and discussion

10. 10 Prediction results Neural networkOutputs Predictions 12345678 One-hidden- layer (18) y1y1 50.00651.01351.98753.97254.9995554.99955 y2y2 711.48112.5077.000811.76514.51514.76416 Heading500751115213540755125515 5516 Two-hidden- layer (8+8) y1y1 5051.004525455 y2y2 711.99611.7267.001712.00414.89314.94616 Heading500751125212540755125515 5516 Two-hidden- layer (14+8) y1y1 5050.99951.99954.00154.99955 y2y2 7.000611.99911.9857.001312.00213.99615.00215.997 Heading50075112521254075512551455155516 Target outputs50075112521254075512551455155516

11. 11 Thanks for your attention!