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首页> 外文期刊>International Journal of Engineering Trends and Technology >Performance analysis of FIR Low Pass FIR Filter using Artificial Neural Network
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Performance analysis of FIR Low Pass FIR Filter using Artificial Neural Network

机译:基于人工神经网络的FIR低通FIR滤波器性能分析

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The aim of this paper is to design and analysis of low pass FIR filter is used for comparison of different types of learning algorithm artificial neural network. A low pass FIR filter is design by using hamming window method with the help of FDA Toolbox in MATLAB. Hamming window is used in this proposed work for achieving minimize the maximum side lobe of signal. It is an optimized Windows method. The five different algorithm of artificial neural network namely generalized regression method, radial basis function, radial basis exact, linear layer and feed forward back propagation are used for comparison. Simulation result of a different artificial neural network has achieved general regression method with the best performance has compare to radial basis function, radial basis exact, linear layer and feed forward back propagation method.
机译:本文的目的是设计和分析低通FIR滤波器,用于比较不同类型的学习算法的人工神经网络。利用汉明窗法,借助MATLAB中的FDA Toolbox设计低通FIR滤波器。在这项建议的工作中使用汉明窗以使信号的最大旁瓣最小。这是一种优化的Windows方法。比较了人工神经网络的五种不同算法,即广义回归法,径向基函数,径向基精确度,线性层和前馈传播。与径向基函数,精确的径向基,线性层和前馈传播方法相比,不同人工神经网络的仿真结果获得了性能最佳的通用回归方法。

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