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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Self-tuning filter"

1

Cao, Songling, and R. Russell Rhinehart. "A self-tuning filter." Journal of Process Control 7, no. 2 (January 1997): 139–48. http://dx.doi.org/10.1016/s0959-1524(96)00024-8.

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2

Mitić, Darko, Goran Jovanović, Mile Stojčev, and Dragan Antić. "ON DESIGN OF SELF-TUNING ACTIVE FILTERS." Facta Universitatis, Series: Automatic Control and Robotics 19, no. 1 (July 28, 2020): 059. http://dx.doi.org/10.22190/fuacr2001059m.

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In this paper, we present one approach in design of self-tuning all-pass, band-pass, low-pass and notch filters based on phase control loops with voltage-controlled active components and analyze their stability as well. The main idea is to vary signal delay of the filter and in this way to achieve phase correction. The filter phase characteristics are tuned by varying the transconductance of the operational transconductance amplifier or capacitance of an MOS varicap element, which are the constituents of filters. This approach allows us to implement active filters with capacitance values of order of pF, making the complete filter circuit to be amenable for realization in CMOS technology. The phase control loops are characterized by good controllable delay over the full range of phase and frequency regulation, high stability, and short settling (locking) time. The proposed circuits are suitable for implementation as a basic building RF function block, used in phase and frequency regulation, frequency synthesis, clock generation recovery, filtering, selective amplifying etc.
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3

JOVANOVIĆ, GORAN, DARKO MITIĆ, MILE STOJČEV, and DRAGAN ANTIĆ. "SELF-TUNING BIQUAD BAND-PASS FILTER." Journal of Circuits, Systems and Computers 22, no. 03 (March 2013): 1350008. http://dx.doi.org/10.1142/s0218126613500084.

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One approach to design self-tuning gm-C biquad band-pass filter is considered in this paper. The phase control loop is introduced to force filter central frequency to be equal to input signal frequency what is achieved by adjusting the amplifier transconductance gm. Thanks to that, the filter is robust to parameter perturbations and it can be used as a selective amplifier. In the full tuning range, it has a constant maximum gain at central frequency as well as a constant bandwidth. The 0.25 μm SiGe BiCMOS technology was used during design and verification of the band-pass filter. The filter has 26 dB gain, quality factor Q = 20 and central frequency up to 150 MHz. Simulation results indicate that the total in-band noise is 59 μV rms , the output third intercept point OIP3 = 4.36 dB and the dynamic range is 35 dB. Maximal power consumption at 3 V power supply is 1.115 mW.
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4

Kladovščikov, Leonid, and Romualdas Navickas. "DESIGN AND INVESTIGATION OF RESISTOR MATRIX FOR ACTIVE ANALOG RC FILTERS." Mokslas - Lietuvos ateitis 12 (January 28, 2020): 1–7. http://dx.doi.org/10.3846/mla.2020.11419.

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Resistor matrixes are widely used in active RC filters as well as in self-tuning systems. Using self-tuning systems for active RC filters, it is possible to automatically tune various parameters of filter – cut-off frequency, gain and quality of filter. Most recent multiband transceivers employ higher order filters for fine bandpass filtering, thus number of passive components increases. In this work, a novel resistor matrix structure and design method is proposed. Proposed resistor matrix structure compensates both integrated circuit process variations and temperature change. Proposed resistor matrix is designed using 0.18 μm TSMC CMOS technology node and investigated using Cadence Virtuoso software. For most accurate comparison of different resistor matrices, all of them were designed in same technology node using design techniques described in other authors’ works.
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5

Liu, Wen Qiang, Gui Li Tao, and Na Han. "Self-Tuning Weighted Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 538 (April 2014): 439–42. http://dx.doi.org/10.4028/www.scientific.net/amm.538.439.

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For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying the recursive instrumental variable (RIV) and correlation method. Then the optimal weighted fusion Kalman filter is obtained by substituting all the fusion estimates into the corresponding optimal Kalman filter. A simulation example shows its effectiveness.
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6

Faiza, Aouiouat A., Sebaa Morsli, and Allaoui Tayeb. "Self Tuning Filter Based Fuzzy Logic Controller for Active Power Filter." Journal Européen des Systèmes Automatisés 53, no. 5 (November 15, 2020): 739–45. http://dx.doi.org/10.18280/jesa.530517.

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The performance of the Active Power Filter (APF) depends on the identification strategy of the reference currents and their control. Among the control strategies proposed in the literature to identifying the reference currents, that based on Self Tuning Filter (STF) which allows extracting directly the voltage and current fundamental components in the α-β axis at high performances, without any Phase Locked Loop (PLL). The performance of STF is function of its proportional parameter. However, there is no technique existed in the literature to dimensioning the proportional parameter of the STF filter. This paper presents an improved method for identifying the reference harmonic currents to be generated by the APF, based on STF for the extraction of harmonic currents and equipped with a Fuzzy Logic Corrector (FLC) in order to improve the waveform of the electric lines currents. The FLC adjust in real time the proportional parameter of the STF filter. The proposed FLC-STF regulator allows to having an optimal extraction of the harmonic currents. The conventional hysteresis method was used for the current control technique to generate the switching sequences of the static switches of the APF voltage inverter. The obtained simulation results, performed under the MATLAB/Simulink® environment on a system feeding a non-linear load, show good performance.
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7

Liu, Wen Qiang, Na Han, Man Yan, and Gui Li Tao. "Self-Tuning Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 229-231 (November 2012): 1768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1768.

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For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. Substituting them into the optimal fusion Kalman filter, a self-tuning fusion Kalman filter for single-channel ARMA signals is presented. A simulation example shows its effectiveness.
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8

Liu, Wen Qiang, Gui Li Tao, Ze Yuan Gu, and Song Li. "Self-Tuning Weighted Measurement Fusion Kalman Signal Filter." Applied Mechanics and Materials 274 (January 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amm.274.579.

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For the single channel autoregressive moving average (ARMA) signals with multisensor and a colored measurement noise, when the model parameters and noise variances are partially unknown, based on identification method and Gevers-Wouters algorithm with a dead band, a self-tuning weighted measurement fusion Kalman signal filter is presented. A simulation example applied to signal processing shows its effectiveness.
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9

MOIR, T. J., T. G. VISHWANATH, and D. R. CAMPBELL. "Real-time self-tuning deconvolution filter and smoother." International Journal of Control 45, no. 3 (March 1987): 969–85. http://dx.doi.org/10.1080/00207178708933781.

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10

Tao, Guili, and Zili Deng. "Self-Tuning Fusion Wiener Filter and Its Convergence." Procedia Engineering 29 (2012): 643–49. http://dx.doi.org/10.1016/j.proeng.2012.01.017.

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Дисертації з теми "Self-tuning filter"

1

Epp, Nelson David. "A self-tuning filter for the mitigation of power system harmonics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0008/MQ60117.pdf.

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2

Ahmeid, Mohamed. "Real-time system identification and self-tuning control of DC-DC power converter using Kalman Filter approach." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3772.

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Switch-mode power converters (SMPCs) are employed in many industrial and consumer devices. Due to the continuous reduction in cost of microprocessors, and improvements in the processing power, digital control solutions for SMPCs have become a viable alternative to traditional analogue controllers. However, in order to achieve high-performance control of modern DC-DC converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. This model can be acquired by means of prior knowledge about the system parameters or using system identification methods. For the best performance of the designed controller, the system identification methods are preferred to handle the model uncertainties such as component variations and load changes. This process is called indirect adaptive control, where the model is estimated from input and output data using a recursive algorithm and the controller parameters are tuned and adjusted accordingly. In the parameter estimation step, Recursive Least Squares (RLS) method and its modifications exhibit very good identification metrics (fast convergence rate, accurate estimate, and small prediction error) during steady-state operation. However, in real-time implementation, the accuracy of the estimated model using the RLS algorithm is affected by measurement noise. Moreover, there is a need to continuously inject an excitation signal to avoid estimator wind-up. In addition, the computational complexity of RLS algorithm is high which demands significant hardware resources and hence increase the overall cost of the digital system. For these reasons, this thesis presents a robust parametric identification method, which has the ability to provide accurate estimation and computationally efficient self-tuning controller suitable for real-time implementation in SMPCs systems. This thesis presents two complete real-time solutions for parametric system identification and explicit self-tuning control for SMPCs. The first is a new parametric estimation method, based on a state of the art Kalman Filter (KF) algorithm to estimate the discrete model of a synchronous DC-DC buck converter. The proposed method can accurately identify the discrete coefficients of the DC-DC converter. This estimator possesses the advantage of providing an independent strategy for adaptation of each individual parameter; thus offering a robust and reliable solution for real-time parameter estimation. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. This thesis also validates the performance of the identification algorithm with time-varying parameters; such as an abrupt load change. Furthermore, the proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Additionally, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical Exponentially weighted Recursive Least Square (ERLS) method. To design a computationally efficient self-tuning controller for DC-DC SMPCs, the second part of the thesis develops a complete package for real-time explicit self-tuning control. The novel partial update KF (PUKF) is introduced for real-time parameter estimation. In this approach, a significant complexity reduction is attained as the number of arithmetic operations are reduced, more specifically the computation of adaptation gains and covariance updates. The explicit self-tuning control scheme is constructed via integrating the developed PUKF with low complexity control algorithm such as Bányász/Keviczky PID controller. Experimental and simulation results clearly show an enhancement in the overall dynamic performance of the closed loop control system compared to the conventional PID controller designed based on a pre-calculated average model. Importantly, in this thesis, unlike a significant proportion of existing literature, the entire system identification, and closed loop control process is seamlessly implemented in real-time hardware, without any remote intermediate post processing analysis.
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3

Novanda, Happy. "Monitoring of power quality indices and assessment of signal distortions in wind farms." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/monitoring-of-power-quality-indices-and-assessment-of-signal-distortions-in-wind-farms(403a470c-279a-4b00-94dc-eaa2507dc579).html.

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Power quality has become one of major concerns in the power industry. It can be described as the reliability of the electric power to maintain continuity operation of end-use equipment. Power quality problems are defined as deviation of voltage or current waveforms from the ideal value. The expansion plan of wind power generation has raised concern regarding how it influences the voltage and current signals. The variability nature of wind energy and the requirements of wind power generation increase the potential problems such as frequency and harmonic distortions. In order to analyze and mitigate problems in wind power generation, it is important to monitor power quality in wind farm. Therefore, the more accurate and reliable parameter estimation methods suitable for wind power generation are needed. Three parameter estimation methods are proposed in this thesis to estimate the unknown parameters, i.e. amplitude and phase angle of fundamental and harmonic components, DC component and system frequency, during the dynamic change in wind farm. In the first method, a self-tuning procedure is introduced to least square method to increase the immunity of the algorithm to noise. In the second method, nonrecursive Newton Type Algorithm is utilised to estimate the unknown parameters by obtaining the left pseudoinverse of Jacobian matrix. In the last technique, unscented transformation is used to replace the linearization procedure to obtain mean and covariance which will be used in Kalman filter method. All of the proposed methods have been tested rigorously using computer simulated data and have shown their capability to track the unknown parameters under extreme distortions. The performances of proposed methods have also been compared using real recorded data from several wind farms in Europe and have demonstrated high correlation. This comparison has verified that UKF requires the shortest processing time and STLS requires the longest.
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4

Maggard, William P. "Adaptive control of flexible systems using self-tuning digital notch filters." Ohio : Ohio University, 1987. http://www.ohiolink.edu/etd/view.cgi?ohiou1183056097.

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5

Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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6

Lin, Wen-Kai, and 林文凱. "Applied Kalman Filter and Model Mismatch Self-Tuning Method to Run-to-Run Control." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/72364493345045699605.

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Анотація:
碩士
國立交通大學
機械工程學系
100
In the last few years, Run-to-Run (RtR) control techniques have been developed and used to semiconductor manufacturing processes to maintain process targets and improve the yield of products. Among the RtR controllers, Exponentially Weighted Moving Average (EWMA), double-EWMA and Predicted Correct Control (PCC) are useful methods for online RtR estimation. However, incorrect selection of the RtR control parameters can have the opposite effect on the controlled process output. Conventional RtR controllers may fail in satisfying performance requirement especially when the system has model mismatch and the environmental perturbation. First, this thesis has discuss and analysis performance of EWMA controller and combine Kalman Filter in run-to-run control to deal with known disturbance : DT、RWD、IMA(1,1)、ARMA(1,1)、ARIMA(1,1,1). Then, a dynamic-tuning control structure having the capability of adjusting the system parameters dynamically that used to unknown disturbance is proposed in this thesis. There are two schemes in this control structure: a model mismatch self-tuning module and Kalman Filter used to estimate disturbance. The control structure, termed Self-Tuning model mismatch Kalman Filter controller (STKF), can reject process disturbance, reduce model mismatch and achieve expected performance.
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7

Amir, Aslanzadeh Mamaghani Hesam. "Design of a Direct-Modulation Transmitter with Self-Optimizing Feedback and a Highly Linear, Highly Reconfigurable, Continuously-Tunable Active-RC Baseband Filter for Multiple Standards." Thesis, 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7479.

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This work consists of two main parts: i) Design and implementation of a compact current-reusing 2.4GHz direct-modulation transmitter with on-chip automatic tuning; ii) Design and implementation of a novel highly-reconfigurable, continuously tunable, power-adjustable Active-RC filter for multiple standards. The design, analysis, and experimental verification of a proposed self-calibrating, current reused 2.4GHz, direct-modulation transmitter are introduced. A stacked arrangement of the power amplifier/voltage-controlled oscillator is presented along with a novel LC-tank-tuning algorithm with a simple, low-cost, on-chip implementation. To transmit maximum power, the tuning loop ensures the PA's resonant tank is centered around the operating frequency, and the loop requires no ADC, DSP, or external signal generator. This work also details the proposed tuning-loop algorithm and examines the frequency-dependent nonlinear power-detector. The system was implemented in TSMC 0.18[mu]m CMOS, occupies 0.7 mm² (TX) + 0.1 mm² (self tuning), and was measured in a QFN48 package on FR4 PCB. Automatically adjusting the tank-tuning bits within their tuning range results in >4dB increase in output power. With the self-tuning circuit active, the transmitter delivers a measured output power of > 0dBm to a 100-[omega] differential load, and the system consumes 22.9 mA from a 2.2-V supply. A biquad design methodology and a baseband low-pass filter is presented for wireless and wireline applications with reconfigurable frequency response, selectable order (1st/3rd/5th), continuously tunable cutoff frequency (1MHz-20MHz) and adjustable power consumption (3mW-7.5mW). A discrete capacitor array coarsely tunes the low-pass filter, and a novel Continuous Impedance Multiplier (CIM) then finely tunes the filter. Resistive/capacitive networks select between the Chebyshev and Inverse Chebyshev approximation types. Also, a new stability metric for biquads, Minimum Acceptable Phase Margin (MAPM), is presented and discussed in the context of filter compensation and passband ripple considerations. Experimental results yield an IIP3 of 31.3dBm, a THD of -40dB at 447mV[subscript pk, diff] input signal amplitude, and a DR of 71.4dB. The filters tunable range covers frequencies from 1MHz to 20MHz. In Inverse Chebyshev mode, the filter achieves a passband group delay variation less than ±2:5%. The design is fabricated in 0.13[mu]m CMOS, occupies 1.53mm², and operates from a 1-V supply.
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8

Fakunle, Daniel. "Comparison of the CUSUM, first-order, self-tuning and Kalman filters." 2010. http://digital.library.okstate.edu/etd/Fakunle_okstate_0664M_10855.pdf.

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Книги з теми "Self-tuning filter"

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Creating a self-tuning Oracle database: Automating Oracle9i Dynamic SGA performance. Kittrell, NC: Rampant TechPress, 2003.

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2

Dynamic feature space modelling, filtering, and self-tuning control of stochastic systems: A systems approach with economic and social applications. Berlin: Springer-Verlag, 1985.

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3

Otter, Pieter W. Dynamic Feature Space Modelling, Filtering and Self-Tuning Control of Stochastic Systems: A Systems Approach with Economic and Social Applications. Brand: Springer, 1985.

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4

Otter, Pieter W. Dynamic Feature Space Modelling, Filtering and Self-Tuning Control of Stochastic Systems: A Systems Approach with Economic and Social Applications. Springer London, Limited, 2012.

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Частини книг з теми "Self-tuning filter"

1

Xu, Xiuling, and Xiaodong Wang. "Hard-Failure Diagnosis Using Self-tuning Kalman Filter." In Communications in Computer and Information Science, 326–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18129-0_52.

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2

Wu, Jian, Dian-guo Xu, and Na He. "Self-tuning Fuzzy Control for Shunt Active Power Filter." In Lecture Notes in Computer Science, 890–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538356_92.

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3

Sun, Xiaojun, and Guangming Yan. "Self-tuning Information Fusion Kalman Filter with Input Estimation." In Recent Advances in Computer Science and Information Engineering, 239–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25778-0_35.

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4

Tao, Guili, and Zili Deng. "Self-tuning Information Fusion Kalman Filter for Multichannel ARMA Signals." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 143–52. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_19.

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5

Liu, Jinfang, and Zili Deng. "Self-tuning Centralized Fusion Wiener Filter with Applied to Signal Processing." In Advanced Electrical and Electronics Engineering, 429–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19712-3_54.

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6

Liu, Jinfang, and Zili Deng. "Self-tuning Information Fusion Wiener Filter for Multisensor Multichannel AR Signals." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 153–62. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_20.

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Tao, Guili, and Zili Deng. "Self-tuning Centralized Fusion Information Filter with Unknown Parameters and its Convergence." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 133–42. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_18.

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8

Shah, Prashant K., Chetan D. Kotwal, and Ashutosh K. Giri. "Power Quality Improvement of Solar Photovoltaic Three-Phase Grid-Interfaced System Under Distorted Grid Conditions Using Self-Tuning Filter-Based Control." In Lecture Notes in Electrical Engineering, 273–84. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1978-6_24.

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Dheenadayalan, Kumar, Gopalakrishnan Srinivasaraghavan, and V. N. Muralidhara. "Self-tuning Filers — Overload Prediction and Preventive Tuning Using Pruned Random Forest." In Advances in Knowledge Discovery and Data Mining, 495–507. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_39.

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"Self-tuning descriptor Kalman filter with correlated noise." In Information Science and Electronic Engineering, 109–14. CRC Press, 2016. http://dx.doi.org/10.1201/9781315265278-29.

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Тези доповідей конференцій з теми "Self-tuning filter"

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Desrochers, Spencer, and Mark Hickle. "Self-Tuning N-Path Filter." In 2021 IEEE/MTT-S International Microwave Symposium - IMS 2021. IEEE, 2021. http://dx.doi.org/10.1109/ims19712.2021.9574807.

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2

Gang Hao and Zili Deng. "Self-tuning Measurement Fusion Kalman Filter." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1712615.

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3

Grimble, M. "Optimal linear filter for self-tuning application." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268491.

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Bai, E. W. "Adaptive identifier with a self-tuning filter." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203391.

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Gao, Yuan, and Zili Deng. "Self-tuning weighted measurement fusion Kalman filter." In 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246495.

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Li, Yun, Jintao Yu, Ming Zhao, and Ke Han. "Self-tuning distributed measurement fusion Kalman filter." In 2010 International Conference on Information and Automation (ICIA). IEEE, 2010. http://dx.doi.org/10.1109/icinfa.2010.5512255.

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Gang, Hao, Jia Wenjing, and Deng Zili. "Self-tuning multisensor measurement fusion Kalman filter." In 2006 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.280580.

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Gao, Yuan, and Zili Deng. "Self-tuning reduced dimension measurement fusion Kalman filter." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554238.

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Wenqiang Liu and Guili Tao. "Multi-model self-tuning weighted fusion Kalman filter." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852693.

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Tahtali, Murat, Andrew Lambert, and Donald Fraser. "Self-tuning Kalman filter estimation of atmospheric warp." In Optical Engineering + Applications, edited by Philip J. Bones, Michael A. Fiddy, and Rick P. Millane. SPIE, 2008. http://dx.doi.org/10.1117/12.795888.

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