Modified Model and Algorithm of LMS Adaptive Filter for Noise Cancellation

Maurya, Awadhesh Kumar; Agrawal, P.; Dixit, Shubhra

doi:10.1007/s00034-018-0952-z

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…Sementara itu, untuk algoritma LMS banyak digunakan oleh peneliti sebagai filter adaptif karena memiliki sifat yang robust, mudah diimplementasikan serta perhitungannya yang cukup sederhana [2]. Namun, algoritma LMS juga banyak digunakan untuk identifikasi parameter suatu plant dan sebagai noise cancellling [3]- [5]. Sukarman [6] telah melakukan simulasi identifikasi parameter pada plant orde dua menggunakan algoritma LMS dengan memperoleh hasil penelitian yang menunjukkan bahwa nilai mean square error (MSE) semakin mengecil dengan bertambahnya jumlah iterasi sedangkan nilai bobot (parameter adaptif) dicapai setelah iterasi ke 600.…”

Section: Pendahuluanunclassified

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Identifikasi Parameter Adaptif Motor Arus Searah Penguat Terpisah Menggunakan Algoritma LMS

Utami,

Hudati

2023

JuLIET

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Sistem kendali adaptif merupakan suatu sistem yang mampu menyesuaikan dirinya terhadap perubahan yang terjadi di lingkungan sekitarnya (beradaptasi). Salah satu pemanfaatan sistem kendali adaptif adalah untuk identifikasi parameter sistem. Motor arus searah penguat terpisah merupakan jenis motor searah yang sumber tegangan jangkar dan tegangan medannya dipasang terpisah. Parameter motor searah penguat terpisah digunakan dalam pemodelan untuk mendapatkan fungsi alih. Dari fungsi alih kontinu kemudian diubah ke dalam bentuk fungsi alih diskrit untuk memperoleh persamaan diferensial diskritnya. Selanjutnya koefisien dari persamaan diferensial tersebut dijadikan sebagai target yang ingin dicapai selama proses iterasi berlangsung pada simulasi algoritma least mean square dengan input xk=rand(1,n), jumlah sampel sebanyak 1000 dan laju konvergensi sebesar 0,05. Pada artikel ini, simulasi dilakukan dengan tujuan untuk mengetahui bobot terbaik, jumlah iterasi dan laju konvergensi yang sesuai untuk pemodelan. Parameter Adaptif yang akan diidentifikasi berupa bobot dari pemodelan adaptif yang dibuat dan hasilnya dibandingkan dengan perhitungan fungsi alih diskrit sebenarnya. Bobot w1 yang ingin dicapai dalam proses iterasi yaitu 0,01566 dan bobot w2=0,0003107. Hasil terbaik yang dapat dicapai dalam proses iterasi yaitu w1=0,015659 dan w2=0,00031161 dengan eror sebesar 0,00000042928 serta nilai MSE (mean square error) sebesar 0,0000000000016.

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Section: Pendahuluanunclassified

“…Algoritma LMS mendapatkan koefisien tapis dengan membuat galat kuadrat rata-rata yang minimum yang disebut sebagai MSE (mean square error). Jika 𝑒 𝑘 dikuadratkan maka diperoleh (3)- (5). Dengan 𝑦 𝑘 = 𝑥 𝑘 𝑇 𝑤 𝑘 = 𝑤 𝑘 𝑇 𝑥 𝑘 , sehingga didapatkan (6).…”

Section: Metodologiunclassified

Identifikasi Parameter Adaptif Motor Arus Searah Penguat Terpisah Menggunakan Algoritma LMS

Utami,

Hudati

2023

JuLIET

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“…With too small an N, the filtering error will be great and the filter just will not serve its filtering purpose or even result in unconvergence of the LMS algorithm. Too great an N, on the other hand, will increase the computational complexity and thus take up more hardware computing resources [27,28].…”

Section: Filter Order Nmentioning

confidence: 99%

The On-Chip D-LMS Filter Design Method of Wireless Sensor Node Based on FPGA

Meng

et al. 2020

Shock and Vibration

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In the real-time position technology of underground shallow source, the signal denoising performance of wireless sensor nodes directly determines the location speed and accuracy of underground burst point. Because of the complexity and randomness of the underground medium and the fact that underground explosion is a nonstationary transient process, the problems of low convergence rate and poor steady-state performance of the filter exist when the existing LMS algorithm is used for signal denoising. In light of the above concerns, this paper comes up with a signal denoising algorithm and hardware implementation method based on D-LMS (delay-LMS). Firstly, according to the autocorrelation function characteristic of random signal, using the principle that the autocorrelation function time delay characteristic of narrowband signal such as explosion vibration signal is better than that of wideband random signal such as ground noise, the D-LMS filter algorithm is constructed by introducing the time delay parameter. Secondly, the selection method of key parameters in D-LMS hardware implementation is analyzed. Thirdly, the corresponding hardware circuit is designed by FPGA, and the simulation is carried out. Numerical simulation and experimental verification show that compared with the existing LMS improved algorithm, the D-LMS algorithm proposed in this paper has higher denoising stability and better denoising effect. Compared with the signal postprocessing method based on the host computer, the signal denoising speed of this method is significantly improved. This method will provide a powerful theoretical method to solve the problem of high-precision and fast source positioning and provide technical support for the development of high-speed and real-time source positioning instruments.

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“…However, a significant dilemma exists between step size variation and convergence to a steady state in LMS, leading to extensive research into various improvements of variable step size LMS adaptive filtering. Maurya et al [13] modified the error equation of the traditional LMS algorithm and achieved improved outcomes in denoising relative to the standard LMS algorithm. Zerguine et al [14] developed a novel normalized least mean square (NLMS) algorithm targeted at signal noise reduction, which involves the mixing of weighted signal and error power.…”

Section: Introductionmentioning

confidence: 99%

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Deng,

Wang

2024

Meas. Sci. Technol.

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To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

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Modified Model and Algorithm of LMS Adaptive Filter for Noise Cancellation

Cited by 16 publications

References 40 publications

Identifikasi Parameter Adaptif Motor Arus Searah Penguat Terpisah Menggunakan Algoritma LMS

Identifikasi Parameter Adaptif Motor Arus Searah Penguat Terpisah Menggunakan Algoritma LMS

The On-Chip D-LMS Filter Design Method of Wireless Sensor Node Based on FPGA

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

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