scholarly journals A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1580
Author(s):  
Junseok Lim ◽  
Keunhwa Lee ◽  
Seokjin Lee
Keyword(s):  
Computational Complexity ◽  
Impulse Response ◽  
Recursive Least Squares ◽  
Mean Square ◽  
L1 Norm ◽  
Actual System ◽  
Rls Algorithm ◽  
Simulation Results ◽  
Norm Penalty ◽  
True System

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

scholarly journals Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 902 ◽  
Author(s):  
Guobing Qian ◽  
Dan Luo ◽  
Shiyuan Wang
Keyword(s):  
Impulsive Noise ◽  
Recursive Least Squares ◽  
Complex Domain ◽  
Superior Performance ◽  
Mean Square ◽  
Kernel Space ◽  
Risk Sensitive ◽  
Simulation Results ◽  
Performance Surface ◽  
Complex Valued

The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS).

scholarly journals A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 135
Author(s):  
Camelia Elisei-Iliescu ◽  
Laura-Maria Dogariu ◽  
Constantin Paleologu ◽  
Jacob Benesty ◽  
Andrei-Alexandru Enescu ◽  
...  
Keyword(s):  
Least Squares ◽  
Impulse Response ◽  
Recursive Least Squares ◽  
Dimensional System ◽  
Third Order ◽  
Rls Algorithm ◽  
Tensor Decompositions ◽  
Rank One ◽  
The Individual ◽  
Least Mean Squares Algorithm

High-dimensional system identification problems can be efficiently addressed based on tensor decompositions and modelling. In this paper, we design a recursive least-squares (RLS) algorithm tailored for the identification of trilinear forms, namely RLS-TF. In our framework, the trilinear form is related to the decomposition of a third-order tensor (of rank one). The proposed RLS-TF algorithm acts on the individual components of the global impulse response, thus being efficient in terms of both performance and complexity. Simulation results indicate that the proposed solution outperforms the conventional RLS algorithm (which handles only the global impulse response), but also the previously developed trilinear counterparts based on the least-mean- squares algorithm.

Change in Impulse Response of Heart Rate to Instantaneous Lung Volume

1994 ◽  
Vol 33 (01) ◽  
pp. 20-21
Author(s):  
H. Mizuta ◽  
Y. Iida ◽  
J. P. Saul ◽  
R. J. Cohen ◽  
K. Yana
Keyword(s):  
Heart Rate ◽  
Impulse Response ◽  
Lung Volume ◽  
Real Data ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Time Data ◽  
Rls Algorithm ◽  
Stationary Conditions ◽  
Real Time Data Processing

Abstract:A method is presented that relates the heart rate variability (HRV) to the change in instantaneous lung volume (ILV) under non-stationary conditions. Methods utilizing a recursive least squares (RLS) algorithm and a modified Widrow LMS algorithm are proposed to keep track of changes in impulse response of HRV to ILV. Results are presented of real data analysis and a dedicated system is proposed utilizing DSP chips for the real time data processing.

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

scholarly journals Adaptive modulation based on steady-state mean square error for underwater acoustic communication

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhiyong Liu ◽  
Zhoumei Tan ◽  
Fan Bai
Keyword(s):  
Steady State ◽  
Mean Square Error ◽  
Adaptive Modulation ◽  
Blind Equalization ◽  
Training Sequence ◽  
Transmission Efficiency ◽  
Underwater Acoustic Communication ◽  
Mean Square ◽  
Modulation Mode ◽  
Simulation Results

AbstractTo improve the transmission efficiency and facilitate the realization of the scheme, an adaptive modulation (AM) scheme based on the steady-state mean square error (SMSE) of blind equalization is proposed. In this scheme, the blind equalization is adopted and no training sequence is required. The adaptive modulation is implemented based on the SMSE of blind equalization. The channel state information doesn’t need to be assumed to know. To better realize the adjustment of modulation mode, the polynomial fitting is used to revise the estimated SNR based on the SMSE. In addition, we also adopted the adjustable tap-length blind equalization detector to obtain the SMSE, which can adaptively adjust the tap-length according to the specific underwater channel profile, and thus achieve better SMSE performance. Simulation results validate the feasibility of the proposed approaches. Simulation results also show the advantages of the proposed scheme against existing counterparts.

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

2016 ◽  
Vol 24 (6) ◽  
pp. 1086-1100
Author(s):  
Utku Boz ◽  
Ipek Basdogan
Keyword(s):  
Computational Complexity ◽  
Vibration Control ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Active Vibration

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF

2014 ◽  
Vol 610 ◽  
pp. 339-344
Author(s):  
Qiang Guo ◽  
Yun Fei An
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Real Time ◽  
Unitary Transformation ◽  
Wiener Filter ◽  
Doa Estimation ◽  
Signal Subspace ◽  
Music Algorithm ◽  
Multi Stage ◽  
Simulation Results

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

scholarly journals Noise reduction in ECG Signals for Bio-telemetryb

2019 ◽  
Vol 9 (1) ◽  
pp. 505
Author(s):  
V. Jagan Naveen ◽  
K. Murali Krishna ◽  
K. Raja Rajeswari
Keyword(s):  
Reducing Power ◽  
Ecg Signal ◽  
Noise Power ◽  
Signal Power ◽  
Mean Square ◽  
Rls Algorithm ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Power Line Interference ◽  
Line Interference

<p><span lang="EN-US">In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error.</span></p>

The effects of CO2 database on a localized AquaCrop model construction based on the field experiment

2021 ◽  
Author(s):  
fawen li ◽  
chunya song ◽  
hua li
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Mean Square Error ◽  
North China Plain ◽  
North China ◽  
Mean Square ◽  
Simulation Results

Abstract To examine whether the use of default CO2 database affected the simulation results, this paper built the AquaCrop models of winter wheat based on the measured CO2 database and the default CO2 database, respectively. The models were calibrated with data (2017–2018) and validated with the data (2018–2019) in the North China Plain. The residual coefficient method (CRM), root mean square error (RMSE), normalized root mean square error (NRMSE) and determination coefficient (R2) were used to test the model performance. The results showed that the accuracy of simulation under the two CO2 database were both good. Compared with the default CO2 database, the simulation accuracy under the measured CO2 database had higher accuracy. In order to verify the model further, the simulated values of evapotranspiration, soil water content and measured values were compared and analyzed. The results showed that there were some errors between the measured evapotranspiration and the values of simulation in the filling and waxing period of winter wheat. In general, the simulation values of evapotranspiration were consistent with the measured value at different irrigation levels. The simulated values ​​of the soil water content at the three levels of irrigation were all higher than the measured values, but the simulated results basically reflected the dynamic changes of soil water content throughout the growth period. The model adjustment value of WP*(the normalized water productivity) were a difference under the two CO2 databases, which is one of the reasons for the difference in the simulation results. The results show that in the absence of measured CO2 data, the default CO2 database can be used, which has little influence on the model construction, and the accuracy of the model constructed meets the actual demand. The research results can provide a basis for the establishment of crop models in North China Plain.

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