Acta Photonica Sinica, Volume. 50, Issue 9, 0930004(2021)
Terahertz Nondestructive Testing Signal Recognition Based on PSO-BP Neural Network
Fig. 1. Defect sample drawing of high temperature resistant composite material with multi-adhesive structure
Fig. 2. Terahertz time domain waveforms of different defect areas
Fig. 3. Operating principle diagram of BP neural network
Fig. 4. Flow chart of optimization process
Fig. 5. Schematic diagram of high temperature resistant composite bonding sample
Fig. 6. Schematic diagram of reflective THZ-TDS
Fig. 7. Comparison of mean square errors of training results between BP neural network and PSO-BP neural network.
Fig. 8. Defect recognition results of BP neural network.
Fig. 9. Defect recognition results of PSO-BP neural network
Fig. 10. Recognition results of different degree of debonding defects by PSO-BP neural network
Fig. 11. Error comparison of PSO-BP neural network in recognizing defects of different degrees
|
|
Get Citation
Copy Citation Text
Meihui JIA, Lijuan LI, Jiaojiao REN, Jian GU, Dandan ZHANG, Jiyang ZHANG, Weihua XIONG. Terahertz Nondestructive Testing Signal Recognition Based on PSO-BP Neural Network[J]. Acta Photonica Sinica, 2021, 50(9): 0930004
Category: Spectroscopy
Received: Apr. 21, 2021
Accepted: Jun. 9, 2021
Published Online: Oct. 22, 2021
The Author Email: