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| Simulation Line Design and Its FPGA Realization Based on BP Neural Network |
| Zhang Hai-yan; Li Xin; Tian Shu-feng |
| Department of Electronic Engineering, Ocean University of China, Qingdao 266100, China |
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Abstract A new method for simulation line realization based on Back Propagation Neural Network (BP NN) is presented in the paper. Applying Genetic Algorithm (GA) to optimize the neural network’s structure, BP NN is trained to correspond the transfer function of simulation line. Activation function of NN is approximated with STAM (Symmetric Table and Addition Method) algorithms. A coaxial-cable which is 10000m long and 55Ω line characteristic impedance is simulated and realized by using FPGA and D/A converter. Experimental results show that the proposed approach can greatly reduce the memory of hardware realization. This method can be generalized to simulate the transmission network with unknown transfer function.
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Received: 15 November 2005
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LIANG Huaguo, SUN Hongyun, SUN Jun, HUANG Zhengfeng, XU Xiumin, YI Maoxiang, OUYANG Yiming, LU Yingchun, YAN Aibin. FPGA-based Soft Error Sensitivity Analysis Method for Microprocessor[J]. JEIT, 2017, 39(1): 245-249. |
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2007, Vol. 29 
