Respiratory Signals Prediction based on Particle Swarm Optimization and Back Propagation Neural Networks
Chang Panchun1, Yang Jimin1*, Yang Juan1, You Tao2
1School of Physics and Electronics, Shandong Normal University, Jinan 250014 China; 2Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, Jiangsu, China
Abstract：Objective, Respiratory motion may cause the change of some organs and tissues, such as lung and liver tumors in radiation therapy, which may influence the treatment effect and increase the damage to normal tissues and organs. Hence, it is an essential work to estimate the real-time movement of target in radiation therapy. Method, BP-NN has been widely used in respiratory motion prediction due to its superior non-linear fitting capability. However, BP-NN is easy to fall into local minimum. Results, In this study a novel method using PSO to optimize the BP-NN was proposed to avoid its drawbacks and improve prediction accuracy. Firstly, the PSO method was used to select optimal initial weights and thresholds of neural network. Then, the optimal initial weights and thresholds was utilized to establish artificial neural network (ANN). Finally, the established PSO-NN was used to predict respiratory signals.The preliminary results of 11 patients demonstrate that the mean absolute error reduced from 0.24 to 0.18(25%) and the coefficient correlation increased from 0.82 to 0.86. Conclusion, The proposed method (PSO-NN)could reduce the risk of BP-NN falling into local optimum and has the ability of improving the prediction accuracy of BP-NN method.
常盼春, 杨济民, 杨娟, 游涛. 基于粒子群算法与反向传播神经网络的呼吸运动预测研究[J]. 中国生物医学工程学报, 2018, 37(6): 714-719.
Chang Panchun, Yang Jimin, Yang Juan, You Tao. Respiratory Signals Prediction based on Particle Swarm Optimization and Back Propagation Neural Networks. Chinese Journal of Biomedical Engineering, 2018, 37(6): 714-719.
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