A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

• Published in: Measurement and control (London) Vol. 56; no. 3-4; pp. 820 - 831
• Main Authors: Shen, LB, Tian, LP
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.03.2023; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Back propagation; Back propagation networks; Composite materials; Composite structures; Fiber composites; Hierarchies; Identification methods; Neural networks; Optical fibers; Particle swarm optimization; Prediction models; Radial basis function; Static loads; Support vector machines; Thresholds; position prediction; optical fiber-composite structures; PSO-BP; Static load
• ISSN: 0020-2940; 2051-8730; 2051-8730
• DOI: 10.1177/00202940221101673

Aiming at researching on health monitoring of composite materials, a static load position identification method for optical-fiber composite structures based on the Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm is proposed. Based on the 2 × 2 optical fibers-composite structures, the PSO-BP algorithm is used to establish a nonlinear mapping between the fiber output intensity and the position. At first, a three-layer BP neural network is established. The number of the hidden layer is 30. And then the PSO algorithm is used to optimize the initial weights and thresholds of the BP neural network. Finally, a BP neural network is built using optimized initial weights and thresholds. A total of 515 sets of data samples are collected by the experimental system, of which 500 sets are used for training and 15 sets are used for the final model prediction. Simulation results show that the Mean Square Error (MSE) of the static load position prediction based on the PSO-BP algorithm is 0.0485. Compared with the position prediction model established by the BP neural network, Radial Basis Function (RBF) neural network and Support Vector Regression Machine (SVRM), the PSO-BP neural network model has a higher accuracy. The proposed method has an important application value for the research of health self-diagnosis of composite structures.