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Design method of high-order limited impulse response digital filter

A technology of digital filter and impulse response, which is applied in the direction of digital technology network, impedance network, biological neural network model, etc., to achieve fast calculation speed, small passband fluctuation, and wide application prospects

Inactive Publication Date: 2011-04-27
HUNAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned technical problems existing in the existing finite impulse response digital filter design, the present invention provides a high-order finite impulse response digital filter design method

Method used

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  • Design method of high-order limited impulse response digital filter
  • Design method of high-order limited impulse response digital filter
  • Design method of high-order limited impulse response digital filter

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application example 1

[0025] In order to facilitate comparison and verification of the effectiveness of the method of the present invention, the present invention is based on the literature [Pavel Zahradnik and Miroslav Fast analytical design algorithms for FIR notch filters], IEEETrans.Circuits and Syst.I, vol.51(3), pp.608-623, 2004] as an example, design an 80-order notch frequency Ω m = 0.35π finite impulse response notch filter.

[0026] First set N=81, ε=+10 -10 ,Η=0.0244, and sample uniformly in Ω∈[0,π] To obtain 41 training samples, for example l = 0, 1, 2, ..., 40, and set the initial weight to be random.

[0027] In order to make the pass band and stop band free of overshoot and fluctuation, two samples are taken in the transition band, and their amplitudes are 0.78 and 0.25 respectively. According to the above steps, after 21 trainings, the neural network converges, and the amplitude-frequency response of the designed finite impulse response notch filter figure 2 . The actual notch filter...

application example 2

[0035] In order to facilitate comparison and verification of the effectiveness of the method of the present invention, the present invention uses the document [Soo-Chang Pei and Peng-Hua Wang. Design of equiripple FIR filters with constraint using a multiple exchange algorithm, IEEE Trans. Circuits and Syst. I, vol. .49(1), pp.113-116, 2002] as an example, design a 77-order finite impulse response with a pass band of [0, 0.4π] and a stop band of [0.45π, π]. Pass filter.

[0036] Let N=78, ε=+10 -5 ,Η=0.02564, and uniform sampling in Ω∈[0,π] To obtain 40 training samples, for example l = 0, 1, 2, ... 39, set the initial weight of the neural network to be random, and take two samples in the transition zone, the amplitudes of which are 0.78 and 0.25, respectively. After 12 trainings, the neural network converges, and the amplitude-frequency response and amplitude-frequency response error of the designed finite impulse response filter can be seen image 3 . It can be seen from the...

application example 3

[0044] To facilitate comparison and verification of the effectiveness of the method of the present invention, the present invention takes Example 2 in the document [IEEE Trans.Circuits and Syst.I, vol.49(1), pp.113-116, 2002] as an example, and designs a 78-order low-pass frequency response linear phase digital differentiator. The low-pass differentiator has an ideal frequency response jΩ in the low frequency band and a zero frequency response in the high frequency band. And the low frequency band boundary frequency is 0.8π, and the high frequency band boundary frequency is 0.85π.

[0045] Let N=79, ε=+10 -10 ,Η=0.05, and uniform sampling in Ω∈[0,π] To obtain 40 training samples, for example The initial weight of the neural network is assumed to be random, and two samples are taken in the transition zone, and their amplitudes are 2.0 and 0.6 respectively. After 5 times of training, the neural network converges, and the amplitude-frequency response and amplitude-frequency respo...

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Abstract

The invention relates to a method for designing a high-order finite impulse response digital filter, which comprises the following steps that: a neural network method is utilized and the amplitude-frequency response error sum of squares of a filter to be designed and an ideal filter serves as the calculation energy function of a neural network; the gradient descent learning algorithm is adopted to train the weight of the neural network to minimize the amplitude-frequency response error sum of squares of the filter to be designed and the ideal filter; when the neural network is stabilized, each parameter of the finite impulse response digital filter can be obtained and then the design of the finite impulse response digital filter is completed. The high-order finite impulse response digitalfilter designed by the method of the invention is characterized by small pass band fluctuation, large stop-band attenuation, controllable boundary frequency and high precision, etc., especially by that no matrix inversion algorithm is required to be carried out in the design, thus having fast operation speed, and the filter has broad application prospect in the engineering fields such as data transmission, high-precision televisions, radar and sonar systems and voice and image processing, etc.

Description

Technical field [0001] The invention relates to a filter design method, in particular to a high-order finite impulse response digital filter design method. Background technique [0002] Finite impulse response digital filters have linear phase characteristics that are difficult to achieve with infinite impulse response digital filters, and have broad application prospects in engineering fields such as data transmission, high-precision television, radar and sonar systems, voice and image processing. [0003] The limited impulse response digital filter realized by the window design method and the frequency sampling design method is not easy to precisely control the passband and stopband boundary frequencies in practical applications. Some design methods, such as the Remez exchange algorithm and linear programming algorithm using the maximum error minimization criterion, need to calculate the inverse of a matrix. When the order of the filter is very high, the inversion of the matrix w...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H17/06G06N3/063
Inventor 何怡刚王小华彭玉楼尹新刘美容邓晓侯周国齐绍忠孙凤
Owner HUNAN UNIV
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