Mechanical properties analysis of cell surface based on genetic simulated annealing optimization neural network

Yifan Gao; Lanjiao Liu; Mingxin Chen; Liguo Tian

doi:10.1117/12.2673514

22 May 2023 Mechanical properties analysis of cell surface based on genetic simulated annealing optimization neural network

Yifan Gao, Lanjiao Liu, Mingxin Chen, Liguo Tian

Author Affiliations +

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401E (2023) https://doi.org/10.1117/12.2673514
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

The surface viscoelastic model was obtained by cell surface mechanics analysis. On this basis, the mechanical data of hepatocytes were obtained by nanoindentation experiment. BP neural network was used to train the mechanical characteristics of cell surface, and the mechanical relationship of stress and strain was obtained. Aiming at the common problem of gradient descent trap and convergence rate of BP neural network, genetic simulated annealing algorithm is introduced to optimize the network weight and improve the prediction accuracy of BP neural network. Finally, the error rate of the neural network model and the traditional model was compared by full scale fitting, which proved that the mechanical model optimized by the neural network could describe the mechanical properties of the cell surface more accurately.

Citation Download Citation

Yifan Gao, Lanjiao Liu, Mingxin Chen, and Liguo Tian "Mechanical properties analysis of cell surface based on genetic simulated annealing optimization neural network", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401E (22 May 2023); https://doi.org/10.1117/12.2673514

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