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Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization
Yan, Xiao-Bo,Xiong, Wei-Qing,Hu, Liang,Zhao, Kuo Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.18
This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.
Yong-An Lai,Pei-Tzu Chang,Yan-Liang Kuo 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.5
The purpose of this study is to demonstrate and verify the application of phase-control absolute-acceleration-feedback active tuned mass dampers (PCA-ATMD) to multiple-degree-of-freedom (MDOF) building structures. In addition, servo speed control technique has been developed as a replacement for force control in order to mitigate the negative effects caused by friction and inertia. The essence of the proposed PCA-ATMD is to achieve a 90° phase lag for a structure by implementing the desired control force so that the PCA-ATMD can receive the maximum power flow with which to effectively mitigate the structural vibration. An MDOF building structure with a PCA-ATMD and a real-time filter forming a complete system is modeled using a state-space representation and is presented in detail. The feedback measurement for the phase control algorithm of the MDOF structure is compact, with only the absolute acceleration of one structural floor and ATMD’s velocity relative to the structure required. A discrete-time direct output-feedback optimization method is introduced to the PCA-ATMD to ensure that the control system is optimized and stable. Numerical simulation and shaking table experiments are conducted on a three-story steel shear building structure to verify the performance of the PCA-ATMD. The results indicate that the absolute acceleration of the structure is well suppressed whether considering peak or root-mean-square responses. The experiment also demonstrates that the control of the PCA-ATMD can be decentralized, so that it is convenient to apply and maintain to real highrise building structures.