基于神经网络的等波纹全通滤波器设计方法
Design of Equiripple Digital All-pass Filters Using Neural Networks Technique
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摘要: 利用Lyapunov能量函数给出了滤波器群延迟误差函数的网络表示方式,当Hopfield网络演变到稳定状态时,即可获得最优的群延迟逼近函数.在此基础上,利用迭代更新网络权函数的方法,并根据具体的精度要求给出迭代终止的准则,实现了滤波器群延迟函数的等波纹逼近.仿真结果表明,这项技术是一种高效的、适合硬件实施的、具有实时性的数字全通滤波器设计方法Abstract: The error representation is reformulated by the Lyapunov energy function reflects the difference between the desired group delay response and the designed response. The optimal filter group delay function is obtained when the neural network is convergence. Furthermore, the proposed method using a weighted updating function can make a very good approximation of the minimax solution. Simulation results indicate that the proposed approach has the advantage of effectiveness and is suitable for hardware implementation in real time