Simulation Line Design and Its FPGA Realization Based on BP Neural Network
基于BP神经网络的仿真线设计及其FPGA实现

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.