微分器优化设计研究

Study on the Optimization Design on the Differentiator

提出了一种基于BP算法的正弦基函数神经网络模型 ,研究了该神经网络算法与线性相位微分器幅频特性的关系 ,证明了该神经网络算法的收敛性 ,给出了微分器的优化设计实例。与传统的雷米兹优化设计方法相比 ,提出的优化设计方法不需要计算矩阵的逆 ,因而解决了雷米兹优化设计方法求高阶矩阵逆的困难。计算机仿真结果表明了该算法模型的微分器的优化设计中不仅是有效的 。

This paper presents a model of sine basis functions neural network based on BP algorithm, discusses the relation between the algorithm of neural network and amplitude-frequency characteristic about the linear phase differentiator, proves the convergence condition of neural-network algorithm, and gives optimal design examples about the differentiator. Compared with Remez optimal algorithm, the optimum design method in the paper need not compute inverse matrix, so solves the difficulty to compute high-degree inverse matrix in Remez optimal design method. The simulation results show that the neural-network algorithm is not only effective but also very good in the field of the differentiator optimal design.