Abstract
The application of medical big data and artificial intelligence algorithms are majorly popular in biomedical field. In this paper, BP neural network optimized by genetic algorithm was used to study the classification of muscle fatigue. Although BP neural network has a strong nonlinear mapping ability by using the gradient descent search method, it is easy to fall into the local minimum during the search process because of the randomness of the initial weights and thresholds generated, which would affect the training rate and the accuracy of muscle fatigue classification. the genetic algorithm was used to complete the configuration of the initial population parameters and the design of fitness function, and the optimal weights and thresholds that met the conditions were output to BP neural network. Finally, the classification results of muscle fatigue were output. The experimental results showed that the GA-BP neural network had a stronger ability to jump out of the local optimization compared with the classification effect of BP neural network. The maximum recognition rate of fatigue state reached 90.4%, and the model running time was 17.1 s, which was relatively reduced by 4.5 s.
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Zang, M., Xing, L., Qian, Z., Yao, L. (2023). Muscle Fatigue Classification Based on GA Optimization of BP Neural Network. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_23
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DOI: https://doi.org/10.1007/978-981-99-3300-6_23
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