基于BP神经网络的仿真线设计及其FPGA实现

该文提出了一种采用BP神经网络实现仿真线的方法。首先采用遗传算法优化神经网络结构，用离线训练后的BP神经网络逼近传输线的传递函数，然后用STAM算法以较少的存储空间实现BP神经网络的激励函数近似，进而用FPGA和D/A转换器进行硬件实现。文中基于FPGA对长度为10000m，特性阻抗为55的同轴电缆进行了仿真线的硬件实现，实验结果验证了该方法的有效性。该方法可以推广到传递函数未知的传输网络的仿真应用中。

Simulation Line Design and Its FPGA Realization Based on BP Neural Network

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.