Recursive Least Research Articles

Detection of Abnormal Operation of PV Inverters Based on Regressive Prediction Models With Recursive Least Squares Training

Detection of Abnormal Operation of PV Inverters Based on Regressive Prediction Models With Recursive Least Squares Training

Method for Prediction of Motion Based on Recursive Least Squares Method with Time Warp Parameter and its Application to Physical Therapy

Method for Prediction of Motion Based on Recursive Least Squares Method with Time Warp Parameter and its Application to Physical Therapy

Analysis of Audio Filtering Algorithms for Noise Cancellation

Real-time signal noise reduction poses a significant challenge in communication systems. To combat intrusive background noise during portable device conversations, an active noise cancellation (ANC) is employed. ANC generates anti-noise signals using techniques such as Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms, Recursive Least Squares (RLS) and modified Recursive Least Squares (M-RLS) for effective reduction of unwanted sound and enhancing the output signal quality. The study includes various real-time noise signals to create anti-noise responses, successfully negating corresponding noise in input signals, which include sine waves, live voices, and music. Through a dedicated micro-controller device and electronic network, an "Anti-noise" wave is introduced to achieve active noise control (ANC). This research identifies ANC module specifications and effectively transmits them through the ANC library, demonstrating the efficiency of the proposed noise reduction technique.

Analysis of the Least Mean Square algorithm with processing delays in the adaptive arm for Gaussian inputs for system identification

This paper analyzes the effect of a processing delay on the Least Mean Squares (LMS) algorithm for a system identification problem when the processing delay is in the adaptive arm of the filter. Thus the sensing of the input signal is delayed. The input is assumed to be a zero mean stationary Gaussian process. The theoretical mean and mean square behavior of the adaptive weight vector is analyzed. The weight vector is shown to be biased and significantly affects the mean square deviation (MSD). Monte Carlo simulations are presented in support of the assumptions used to derive the theoretical model as a function of the delay, bandwidth and step-size for the LMS algorithm. The results suggest bias problems with other more complicated adaptive filtering algorithms such as Normalized LMS and Recursive Least Squares.

Bidirectional EMD-RLS: Performance analysis for denoising in speech signal

Empirical Mode Decomposition (EMD) is an alternative decomposition method that allows the analysis of non-stationary and non-linear signals, decomposing them into components called Intrinsic Mode Functions (IMFs). In this paper, we introduce the bidirectional combination between EMD and the Recursive Least Squares (RLS) adaptive filter and compare it to Least Mean Square (LMS), EMD-LMS and RLS methods. The hybrid algorithm relies on the determination of vertical (IMFs indexes) and horizontal (temporal indexes) weighting coefficients. We evaluated the effectiveness of the methods by filtering sets of speech signals artificially corrupted by white Gaussian noise. To find the method that provides the greatest noise reduction and the best speech quality, we took into account the Signal-to-Noise Ratio and the Perceptual Evaluation of Speech Quality (PESQ) score. The analyzed cases showed that EMD-RLS can provide results as good as the other methods, but with a shorter learning curve than LMS and EMD-LMS, despite the higher computational cost. For a single training iteration, the methods presented PESQ scores on average equivalent to each other. However, they showed a significant difference in performance regarding noise reduction, with the best result obtained by RLS, followed by EMD-RLS, EMD-LMS and LMS.

Identification of Wiener Systems with Recursive Gauss-Seidel Algorithm

The Recursive Gauss-Seidel (RGS) algorithm is presented that is implemented in a one-step Gauss-Seidel iteration for the identification of Wiener output error systems. The RGS algorithm has lower processing intensity than the popular Recursive Least Squares (RLS) algorithm due to its implementation using one-step Gauss-Seidel iteration in a sampling interval. The noise-free output samples in the data vector used for implementation of the RGS algorithm are estimated using an auxiliary model. Also, a stochastic convergence analysis is presented, and it is shown that the presented auxiliary model-based RGS algorithm gives unbiased parameter estimates even if the measurement noise is coloured. Finally, the effectiveness of the RGS algorithm is verified and compared with the equivalent RLS algorithm by computer simulations.

Research on attitude compensated algorithm for shipborne dynamic weighing

Shipborne dynamic weighing is fundamental in developing marine fishery resources and oceanographic research. It enables the weighing and sorting of seafood, quantitative baiting, and measurement of research sample weights in marine environments. Therefore, developing shipborne dynamic weighing systems is crucial for the integrated exploitation of marine fishery resources. However, research on shipborne dynamic weighing is limited. To address this issue, the study initially analyzed the impact of ship’s attitude information on weighing results. Subsequently, a mathematical model for shipborne dynamic weighing, incorporating compensation factors, was constructed. The compensation factors were determined using the Recursive Least Squares (RLS) method. And then real-time weight estimation was updated using Kalman filtering, effectively mitigating the influence of ship oscillations and swaying on weight measurements. Furthermore, a shipborne dynamic weighing system with a 24-bit analog-to-digital converter (ADC) and STM32F4 processor was developed. The system performance was evaluated by simulating the sail of a ship under different sea conditions on a swing platform. The results demonstrate that the average absolute percentage error of the test meets the requirement of less than 1%, and the standard deviation of the error is less than 1% F. S., which essentially meets the weight measurement requirements of marine dynamic weighing and sorting.

Efficient enhanced Recursive Least Square algorithm adaptive filtering scheme for artifacts removal in ECG signals

This paper proposes an improved Recursive Least Square (RLS) algorithm to address the limitations of the conventional RLS algorithm, which suffers from poor performance when dealing with non-stationary noise. The proposed algorithm, along with various algorithms, have been designed using Xilinx System Generator (XSG). These algorithms have been tested to denoise ECG signals that have been corrupted with four types of real noise obtained from the MIT-BIH dataset. The improved algorithm utilizes a systolic architecture that enables faster processing and better noise reduction capabilities. The experimental results demonstrate that the proposed algorithm with and without systolic architecture outperforms the conventional RLS algorithm and other state-of-the-art algorithms in terms of signal-to-noise ratio (SNR), mean squared error (MSE), and convergence speed.

AdaBoost Algorithm-Based Channel Estimation: Enhanced Performance

A combination of a group of rules characterized by weakness and imprecision, to reach a prediction rule known for its high precision, forms the concept of boosting, a machine learning approach. From this concept, the Adaptive Boosting (AdaBoost) algorithm spun. It is the first of its kind, and remains in use, under study, and involved in practical applications in various fields to this day. This algorithm is involved in modulation techniques, as it works on improving the bit error rate (BER). Inputting a noisy signal received from a sender into AdaBoost yields the original signal after removing noise. This is done through the reconstruction of the constellation diagram of the modulation technique, by eliminating the noise filling the data’s signal space. The result is an enhancement of the BER of 8 quadrature amplitude modulation (8QAM) and 16 quadrature amplitude modulation (16QAM), through AdaBoost. The AdaBoost algorithm is then added to the channel estimation techniques like the Least Squares (LS), Least Mean Squares (LMS) and Recursive Least Squares (RLS) and is utilized to enhance the BER performance of different estimation techniques in a Rayleigh fading environment. The AdaBoost technique allows benefiting from its learning capabilities as a machine learning algorithm in overcoming the effect of noise and fading channel on the received signals.

State of Charge Estimation of Lithium-Ion Batteries Based on Vector Forgetting Factor Recursive Least Square and Improved Adaptive Cubature Kalman Filter

Accurate online parameter identification and state of charge (SOC) estimation are both very crucial for ensuring the operating safety of lithium-ion batteries and usually the former is a base of the latter. To achieve accurate and stable SOC estimation results, this paper proposes a model-based method, which incorporates a vector forgetting factor least square (VFFLS) algorithm and an improved adaptive cubature Kalman filter (IACKF). Firstly, considering it is difficult for the traditional forgetting factor recursive least square (FFRLS) algorithm to balance the accuracy, convergence, and stability for multiple parameters with different time-varying periods, an improved VFFLS method is employed to determine the multiple parameters of the first-order RC battery model online. It supersedes the single forgetting factor in the FFRLS with multiple forgetting factors in a vector form for improving adaptive capability to multiple time-varying parameters. Secondly, aiming at the fact that the standard cubature Kalman filter (CKF) cannot operate properly when the error covariance matrix is non-positive definite, which is caused by disturbance, initial error, and the limit of the computer word length, the UR decomposition rather than the Cholesky decomposition is applied, thus improving the algorithm stability. In addition, an adaptive update strategy is added to the CKF to enhance accuracy and convergence speed. Finally, comparative experiments with different operating patterns, positive and non-positive definite error covariance matrices, and temperatures are carried out. Experimental results showed that the proposed method can estimate the SOC accurately and stably.

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

The artificial intelligence techniques such as neural networks and fuzzy systems play an important role to disconnect flexion & expansion of the swing leg, the earth response force of the other foot has been redesigned. Underthat paper, we think the fuzzy controller plan issue for yield following flawed genuine investigation of nonlinear systems. For examination, an essential fuzzy control plot has been bristly developed dependent on a current methodology delegate under the field.In this paper, the Feedforward Neural Network has been implemented with integer, fixed point and floating point data representations. Additionally, The Fuzzy Logic Controllers in both analog and digital forms has been implemented in hardware. Both designs use less hardware resources and operate with reasonable speed compared to other existing designs. The digital implementation of Fuzzy Logic Controller has been tested for a simulated first order liquid level process and the performance results have been compared with those of the Matlab version of Fuzzy Logic Controller. Here, Fuzzy Logic Controller is used as the controller and is trained adaptively for the changes in process parameters using recursive k-means clustering algorithm for updating the centers of the hidden layer and Recursive Least Square algorithm for updating the weights of the output layer, the result of the settling time about 20ms and takes 20 iterations, and the squared error reaches zero at approximately 20 µs. Index Terms— Fuzzy Controller, leg interaction force, Prosthetic legs, Human walking, GyroAccelerate,Fuzzy Logic System.

Efficient FPGA Implementation of Recursive Least Square Adaptive Filter Using Non- Restoring Division Algorithm

Efficient FPGA Implementation of Recursive Least Square Adaptive Filter Using Non- Restoring Division Algorithm

Enhancement Cascaded Least Square of RLS and NLMS for Wireless Communication System

Orthogonal Frequency Division Multiplexing or known as OFDM has been a prominent digital communication scheme and was introduced in the 4G network. This technique can be described as an exceptional multicarrier instance where the signal data is transmitted using separate subcarriers through a total of the subcarriers at a lower rate. Although OFDM seems perfect for the development of 5G networks and upcoming 6G, this type of digital modulation still suffers from intersymbol interference (ISI). Enhancement Cascaded Least Square (ECLS) which is a sequence of adaptive algorithms has been proposed in this research to overcome ISI that occurs due to multipath propagation. ECLS is a combination of updated Recursive Least Square (URLS) and Normalized Least Mean Square (NLMS). URLS will reduce complexity while NLMS will overcome problem of convergence before being combined to become ECLS. The research outcome will be illustrated in Bit error rate (BER) to analyse lower interference and Mean Square Error (MSE) to analyse convergence. This research justified that ECLS can minimize the existence of interference in wireless communication systems.

Performance of Recursive Least Squares Algorithm Configurations for Online Parameter Identification of Induction Machines in an Automotive Environment

The recursive least squares algorithm for online parameter identification of induction machines has a high potential to serve as a basis for an innovative electric vehicle diagnosis concept. Commonly used for control parameter tuning, this approach is established in numerous industrial applications. However, in the automotive environment, special machine designs are used and highly dynamic operation takes place in a wide speed and load range. This results in fast transient parameter behavior, which is challenging in terms of online parameter identification. Therefore, the algorithm performance must be rated in our field of application with suitable dynamic test profiles. In this work, we compare several algorithm extensions to a novel recursive least squares algorithm with multiple variable forgetting factors for induction machines. The algorithms are analyzed regarding their handling of the associated transient parameter behavior. In addition, different identification model structures are considered to deal with the dynamic speed operation and the associated transient iron losses. Special attention is given to the real-time performance of the overall identification algorithms as this is a major requirement for implementation in automotive embedded systems. For validation, both, simulation and experimental results are presented, and associated configuration recommendations are provided.

Research on a High-Precision State-of-Charge Estimation Method Based on Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Applied to LiFePO4 Battery

The state-of-charge (SOC) estimation accuracy is closely associated with the estimation method and the battery parameter identification performance. The battery parameter identification method based on forgetting factor recursive least squares (FFRLS) has the advantages of high parameter identification accuracy and fast dynamic response speed. On this basis, the performance of two SOC estimation methods, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) are compared and studied. The results show that AEKF has better steady-state and dynamic SOC estimation performance, but the estimation accuracy and dynamic response performance are still not objective. To further improve the performance of SOC estimation, a joint SOC estimation method based on FFRLS-AEKF is proposed, and the SOC estimation experimental results with FFRLS-AEKF and AEKF are conducted. The experimental results show that the proposed joint SOC estimation method based on FFRLS-AEKF has a better steady-state and dynamic performance of SOC estimation. The maximum absolute error of the proposed algorithm is 4.97%. As the battery working time increases, the SOC estimation accuracy continues to converge to the true value, and the average absolute error is reduced to 2.5%. The proposed method and theoretical analysis are proven to be correct and feasible.

An Improved Recursive Least Square Algorithm For Adapting Fuzzy Channel Equalizer

Adaptive filters have been thoroughly investigated in digital communication. They are especially exploited as equalizers, to compensate for channel distortions, although equalizers based on linear filters perform poorly in nonlinear distortion. In this paper, a nonlinear equalizer based on a fuzzy filter is proposed and a new algorithm for the adaptation parameters is presented. The followed approach is based on a regularization of the Recursive Least Square (RLS) algorithm and an incorporation of fuzzy rules in the adaptation process. The proposed approach, named Improved Fuzzy Recursive Least Square (IFRLS), enhances significantly the fuzzy equalizer performance through the acquisition of more convergence properties and lower steady-state Mean Square Error (MSE). The efficiency of the IFRLS algorithm is confirmed through extensive simulations in a nonlinear environment, besides the conventional RLS, in terms of convergence abilities, through MSE, and the equalized signal behavior. The IFRLS algorithm recovers the transmitted signal efficiently and leads to lower steady-state MSE. An improvement in convergence abilities is noticed, besides the RLS.

Implementation of Real-time Parameter Estimation in Labview and Dsp

This paper presents the results of real time aircraft parameter estimation technology in an aircraft real time simulator. The equation error Recursive Least Squares (RLS) method is used for performing the parameter estimation. The RLS technique is successfully adapted for on-line parameter estimation by a novel methodology that will work when measurements of state derivatives are not available. Subsequently, the algorithm is implemented in Lab VIEW and in digital signal processor (DSP) hardware to demonstrate its suitability for further implementation at flight test centers and for on-board parameter estimation for aircraft.

A Recursive Least P-Order Algorithm Based on M-Estimation in a Non-Gaussian Environment

In recent years, adaptive filtering algorithms based on M-estimation can effectively handle non-Gaussian noise, but large outliers still exist which will seriously disrupt the performance of the algorithm. Here, the recursive least p-Order algorithm based on M-estimation (MRLP) is proposed. The MRLP combines the advantages of M-estimation and the lp norms. The proposed MRLP can mitigate the impact of large outliers and exhibit strong robustness when suffering from non-Gaussian noise, and can also be well applied to tracking signals. Numerical simulations verify the excellent performance of the proposed algorithm.

Equalizer of Amplitude-Frequency Response of Acoustic Channels and its Adaptive Filter Based on Recursive Least Squares Algorithm with Diagonalized Correlation Matrix

The paper presents an equalizer of the amplitude-frequency response of the acoustic wave propagation channels in the rooms. The considered equalizer uses the so-called modified architecture based on the adaptive filter with the input signal filtering, which weights are calculated using the Recursive Least Squares (RLS) algorithm. The equalizer of the acoustic channels amplitude-frequency response usually requires the usage of the adaptive filters with the large number of weighs that means the large computational complexity of the adaptive filter. The adaptive filters based on the fast RLS algorithms are efficient in the terms of their complexity but often unstable if the number of adaptive filter weights is large. Therefore, the algorithms are poorly suited for usage in the amplitude-frequency response equalizers of the acoustic channels. Adaptive filters based on the RLS algorithm, which use the Matrix Inversion Lemma (MIL), are stable, but computationally complex. The equalizer presented in this paper uses a simplified adaptive filter based on the MIL RLS algorithm with a diagonalized correlation matrix of the filter input signal. In addition, in the filter, each of the square submatrices on the simplified correlation matrix diagonal is inverted using a computationally efficient version of the MIL that takes into account the symmetrical structure of the mentioned submatrices relatively their main diagonals. Computer simulation confirms the performance of the proposed equalizer and demonstrates its efficiency comparing to the unsimplified equalizer. The demo includes the graphs of the equalizer steady-state amplitude-frequency response, the acoustic channel amplitude-frequency responses before and after the equalization, and the power spectral density plots of the equalizer input signal and the signals passed through the equalizer and the acoustic medium. The equalizer can be used in the equipment for the high-quality voice and music reproduction in the conditions of the limited computing resources for the implementation of the equalizer.

Wearable Integrated Online Fusion Learning Filter for Heart PPG Sensing Tracking

Heart rate is an important physiological indicator of the human body. It can evaluate the activity of the heart and the degree of fatigue. It can be further used to assess emotions by monitoring heart rate changes and voice recording or breathing. Single-lead or multi-lead equipment is often used to monitor ECG in medical treatment. However, it brings inconvenience to daily life to record the heart rate from a free-living natural environment. Due to the subject’s voluntary or involuntary movement, it is difficult to collect ideal photoplethysmography (PPG) signal by using wearable device as it is continually interfered by the motion artifacts (MA). These MAs seriously affect the calculation and online monitoring of the heart rate. This paper proposes a wearable device with real-time online fusion learning for heart PPG tracking. In particular, it proposes a Recursive Least Squares algorithm to fuse three-axis acceleration signals to remove motion artifacts from PPG signals. In order to further determine the heart rate frequency range and select the appropriate spectrum peak, a decision tree spectrum interval estimation method is proposed to provide prior information for PPG signal spectrum tracking, so as to optimize the spectrum peak estimation. The performance of the proposed device, which is an integrated internet of things with wearable platform, has been shown to be robust to natural and unpredictable situations. Experimental results have confirmed that the proposed method can adaptively track the PPG signal with high accuracy.