دو الگوریتم فیلتر انطباقی زیر باند بهبود یافته با کاهش پیچیدگی محاسباتی
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دو الگوریتم فیلتر انطباقی زیر باند بهبود یافته با کاهش پیچیدگی محاسباتی

عنوان فارسی مقاله: دو الگوریتم فیلتر انطباقی زیر باند دارای ساخت چند باندی بهبود یافته با کاهش پیچیدگی محاسباتی
عنوان انگلیسی مقاله: Two improved multiband structured subband adaptive filter algorithms with reduced computational complexity
مجله/کنفرانس: پردازش سیگنال - Signal Processing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، مهندسی نرم افزار
کلمات کلیدی فارسی: بهبود فیلتر انطباقی زیر باند دارای ساخت چند باندی، عملکرد میانگین مربعات، رگرسور انتخابی، نرخ همگرايی، پيچيدگی محاسباتی
کلمات کلیدی انگلیسی: Improved multiband structured subband adaptive filter، Mean-square performance، Selective regressors، Convergence rate، Computational complexity
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.sigpro.2018.08.001
دانشگاه: Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, P.O.Box: 16785-163, Iran
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 3/933 در سال 2017
شاخص H_index: 105 در سال 2019
شاخص SJR: 0/940 در سال 2017
شناسه ISSN: 0165-1684
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 36
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11144
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Background on IMSAF


3- The SR-IMSAF algorithm


4- The DSR-IMSAF algorithm


5- General update equation


6- Extension of the framework


7- Mean-square performance analysis of the family of IMSAF algorithms


8- Mean and mean-square stability of the family of IMSAF algorithms


9- Computational complexity


10- Simulation results


11- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


The improved multiband structured subband adaptive filter (IMSAF) utilizes the input regressors at each subband to speed up the convergence rate of MSAF. When the number of input regressors is increased, the convergence rate of the IMSAF algorithm improves at the cost of increased complexity. The current study introduces two new IMSAF algorithms with low computational complexity feature. In the first algorithm, a subset of input regressors at each subband is optimally picked out during the adaptation. In the second approach, the number of selected input regressors is dynamically changed at each subband for every iteration. The introduced algorithms are called selective regressor IMSAF (SR-IMSAF) and dynamic selective regressor IMSAF (DSR-IMSAF). The SR-IMSAF and DSR-IMSAF are shown to be capable of outperforming the full-update IMSAF while the computational complexity is kept low. In the following, the general update equation to establishment of the family of IMSAF algorithms is presented. Accordingly, the mean-square performance analysis of the algorithms is studied in a unified way and the general theoretical expressions for transient, steady-state, and the stability bounds for IMSAF, SR-IMSAF, and DSR-IMSAF are derived. The good performance of the introduced algorithms and the validity of the derived theoretical relations are justified by presenting various experimental results.


Introduction


Adaptive filters are applied in many applications such as system identification, channel equalization, signal prediction, and noise cancellation [1], [2], [3]. In these applications, the generated signals are processed to identify the impulse response of the unknown system. This objective is successfully achieved by using adaptive filters rather than conventional digital filters. The adaptive filters utilize a recursive algorithm to design itself. The algorithm updates the filter coefficients through successive iterations and finally converges to the optimal Wiener-Hopf solution. The performance of an adaptive filtering algorithm is evaluated by the rate of convergence, misadjustment, and computational complexity features. The conventional LMS adaptive filter algorithm has the advantage of being very simple; it is easy to implement; and it has a very low computational complexity. However, when the input signal is highly colored, the LMS convergence slows down [3], [4]. To improve the convergence behavior of the LMS, various adaptive algorithms such as affine projection algorithm (APA) and multiband-structured subband adaptive filter (MSAF) were proposed [5], [6]. To increase the convergence speed of MSAF, the variable step-size MSAF (VSS-MSAF) was introduced [7]. Due to VSS, the computational complexity in VSS-MSAF increases.

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