Least Mean Square Adaptive Algorithm Research Articles

Magnetic Interference Compensation Using the Adaptive LMS Algorithm

The aim of this study is to evaluate the method for the compensation of magnetic field interference from the mains, which consists primarily of components with a frequency of 50 (60) Hz and its multiples. The setup compensates for the interfering field using three Helmholtz coils, magnetic field sensors, a digital processing unit, and power amplifiers. The processing unit implements an adaptive least-mean-square (LMS) algorithm to calculate the currents through coils that are necessary for compensation. The prototype fully compensates for interfering with the magnetic field at 50, 150, 250, and 350 Hz down to the noise level of the sensor arrangement within 20 s and reduces higher harmonics. The overall reduction in the rms value of the interfering field is better than 28 dB, and a significantly better reduction is achieved in the frequency range from 0 Hz to 400 Hz.

Adaptive Least Mean Square Controller for Power Quality Enhancement in Solar Photovoltaic System

The objective of the proposed work is to develop a Maximum Power Point Tracking (MPPT) controller and inverter controller by applying the adaptive least mean square (LMS) algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is given by its simplicity and reduced required computational time. The adaptive LMS algorithm is applied to modify the Perturb and Observe (P&O), MPPT controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The adaptive LMS maximum power point tracking controller gives better optimal solutions with less steady error 0.7% (6 watts) and 0% peak overshot in power with the tradeoff being more settling time at 0.33 s. The development of the inverter control law is performed using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and, later, to compare them with the actual grid currents. The result of this comparison is used to generate the switching gate pulses for the inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in grid and load current (3.25% and 7%) and voltage (0%) is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is performed by selecting step incremental values and sampling time.

A novel positioning method for magnetic spiral-type capsule endoscope using an adaptive LMS algorithm

In this paper, a novel positioning method for the wireless capsule endoscope (WCE) is proposed. An up-down symmetric array of magnetic sensors is used to detect magnetic signals from an external permanent magnet (EPM) for active control and mixed magnetic signals (the EPM and the WCE), and the adaptive least mean squares (LMS) algorithm is applied. Firstly, the number of iterations is determined by comparing the cancellation effect of input signals of different lengths. Subsequently, to separate the WCE’s magnetic signals from the mixed magnetic signals, the data obtained from the magnetic sensor arrays are processed in weighted iterations. The method has been applied to the actual experimental platform. From the experimental results achieved in this work, the average relative errors of the WCE’s triaxial signals were found to be 2.04 %, 2.20 %, and 1.47 % respectively. Achieved results demonstrate the feasibility and rationality of the positioning method discussed in this work. Moreover, the method can solve the problem of strong magnetic interference when the EPM provides active control to the WCE. It plays an essential role in driving the realization of closed-loop control of the WCE.

Design of FIR Filter Using Adaptive LMS Algorithm for Energy Efficient Application

This paper presents the LMS (Least Mean Square) sub-channel. Minor square calculations have been used in many DSP programs to address the signal and are intended to create channel coefficients based on the adjusted signal. The proposed strategy reduces energy and regional consumption due to the modification of locally produced loads in terms of revised yields and previous loads. The cargo area decided to use the LMS calculation. Suggested production was completed using Modalism, a coded structure using Verilog HDL and compiled by Xilinx Vivado. Power is determined by using the Xilinx power tester with a focus on FPGA. The significant results of the proposed framework have improved the region and capacity compared to the standard FIR channel.

Gray Encoded Harmonics Power Line Interference Cancelling Structure Using LMS and NLMS Adaptive Algorithms

This paper proposes the implementation of a Gray encoded structure for harmonics power line interference cancelling. The structure uses dedicated hardware architecture for the Least Mean Square (LMS) adaptive filtering algorithm, as well as its normalized version (NLMS). In the used scheme, from a 60Hz reference signal, the algorithms are able to estimate the superior harmonics, using after these results for the cancelling of interferences related to the signal of interest. In this work, the proposed adaptive filtering architectures and the harmonics generator block use a Hybrid encoding in its data buses, which is a compromise between the minimal input dependency presented by the Binary encoding and the low switching characteristic of the Gray encoding. For the Hybrid structures, new Hybrid multipliers were proposed, and the results showed that those multipliers are more efficient than the Binary ones, by presenting less power consumption in some cases. The implemented harmonic cancelling structure with the LMS and NLMS adaptive filtering architectures were validated and compared by using both Binary and Hybrid encoding. The efficiency of the implemented Hybrid structure for the cancelling of interferences was proved by reducing more power than the Binary one. By the results, we conclude that it could be practicable to implement a harmonic cancelling structure, based on LMS and NLMS adaptive filtering, operating on Hybrid encoding.

A Study on Active Noise Reduction of Automobile Engine Compartment Based on Adaptive LMS Algorithm

The engine compartment is the main source of automobile noise. In this paper, based on the study of the noise characteristics of the engine compartment, the relationship model is established between engine speed and noise frequency, and an active noise reduction method is proposed based on the adaptive least mean square (LMS) algorithm. Because the adaptive change of the convergence factor is controlled by noise error, the method can reduce the steady-state error of the algorithm and improve the convergence speed of the algorithm. Through the analysis of single-channel active noise reduction control method, we deduced that this method can also realize multi-channel and multi-frequency point active noise reduction. Simulation and experimental results show that the convergence rate and steady-state error of the adaptive LMS algorithm are both taken into account in our method; the two-channel noise reduction experiment also shows that the total sound pressure level of channel one and channel two is reduced by 4.6 and 9.6 dB, respectively, which fully shows the feasibility of the multi-channel active noise reduction method based on the adaptive LMS algorithm.

Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects.

Adaptive moment estimation (Adam) is a popular optimization method to estimate large-scale parameters in neural networks. This paper proposes the first use of Adam algorithm to fast and stably converge large-scale tap coefficients of polynomial nonlinear equalizer (PNLE) for 129-Gbit/s PAM8-based optical interconnects. PNLE is one of simplified Volterra nonlinear equalizer for making a trade-off between complexity and performance. Different from serial least-mean square (LMS) adaptive algorithm, Adam algorithm is a parallel processing algorithm, which can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Timing error is one of the main obstacles to the PAM systems with high baud rate and high modulation order. Owing to parallel processing and global optimization, Adam algorithm has much better performance on resisting the timing error, which can achieve faster, more-stable and lower-MSE convergence compared to LMS adaptive algorithm. In conclusion, Adam algorithm shows great potential for converging the tap coefficients of PNLE in PAM8-based optical interconnects.

The Grid Voltage’s Asymmetrical Fault Detection Method based on Variable-Step LMS Adaptive Notch Filter Algorithm

For wind turbine systems (WTS), the grid voltage fault detection algorithm determines the response speed of WTS to grid voltage fault. This paper proposed a sort of voltage asymmetrical fault detection method, to detect negative-sequence component in grid voltage, variable-step (VS) least mean square (LMS) adaptive notch filter (ANF) is applied to eliminate the 2nd-harmonic frequency component. The application of ANF can separate negative-sequence component from grid voltage, therefore the voltage asymmetrical fault can be detected; the self-tuning of controllable parameters of ANF can be realized by the LMS adaptive algorithm, which is convenient and efficient in practical application; furthermore, because the variable step size adjustment is adopted, the contradiction between convergence rate and steady-state error of ANF can be solved. The MATLAB/Simulink simulation and FFT analysis results are in conformity with the theorical analysis, which proves the feasibility of the method.

Development of Multi-Staged Adaptive Filtering Algorithm for Periodic Structure-Based Active Vibration Control System

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles.

A highly scalable parallel spike-based digital neuromorphic architecture for high-order fir filters using LMS adaptive algorithm

This brief presents a highly scalable parallel neuromorphic architecture to efficiently compute high-order adaptive FIR filters using a least mean square (LMS) algorithm. This has been achieved by eliminating critical paths because they critically affect the scalability of advanced parallel architectures. Here, the scalability is defined in terms of number of taps and bit-length. On the computation of high-order adaptive FIR filters, the multiplication is the most demanding operation. Therefore, we have made intensive efforts to create a compact new neural multiplier by improving the design and hardware implementation of an existing neural multiplier. The resulting multiplier requires 50 % fewer synapses, 40 % fewer neurons, 30 % fewer area resources and 26 % fewer clock cycles compared with the existing neural multiplier, respectively. The efficient implementation of the proposed multiplier has allowed us to eliminate critical paths significantly and thus the bit-length can be easily increased to guarantee the convergence performance when high-order adaptive filters are processed. To demonstrate its effectiveness, the proposed multiplier was included in the neuromorphic architecture to support high-order adaptive FIR filters. In addition, we employ the time multiplexing technique to maximize the utilization of the proposed neural multiplier by performing filter processing and the adaptive process because multiplication is involved in both. We mainly use this strategy to eliminate critical paths and reduce the area consumption by implementing a large number of taps. The proposed neuromorphic architecture was implemented on the Kintex-7 Field Programmable Gate Array (FPGA) development kit to validate its performance. Our results demonstrate that the neuromorphic architecture is capable of processing higher adaptive FIR filters compared with previously reported solutions. This potentially allow its practical use in many advanced digital signal processing applications such as acoustic echo cancellers, active noise control, channel equalization and system identification.

Passive detection parameter measurement of azimuth and frequency based on single vector sensor

This paper mainly studies the measurement technology of passive detection parameters based on single vector sensor. Ship radiated noise contains a lot of target information, so the extraction of the line noise of the ship's radiated noise is of great significance in the measurement of passive detection parameters. The radiation noise of the ship is a broadband spectrum modulated by the spectral signal envelope. In this paper, the mathematical model of the radiation noise of the ship is simulated and the demodulation of the ship's radiated noise is carried out by using the square demodulation and the absolute value demodulation. The average azimuth and complex intensifier are used to estimate and analyze the target azimuth, and the estimated angle is statistically processed by histogram and weighted histogram. For the frequency estimation technique, the adaptive frequency estimator based on the adaptive notch filter and the adaptive LMS (least mean square) algorithm is chosen to study the target signal frequency.Through the verification of the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, so it is of great significance in the research of underwater acoustic detection technology.

Research on passive detection parameter measurement technology based on single vector sensor

Passive detection as a major function of the sonar system, over the years has been the work of waterborne workers to solve the problem. This paper mainly talks about the measurement technology of passive detection parameters based on single vector sensor. The problem of low frequency line spectrum signal extracted from ship radiated noise is studied, and the azimuth sequence and frequency sequence are obtained by estimating and analyzing the low frequency line spectrum signal. Ship radiated noise contains a lot of target information, so the extraction of the line noise of the ship's radiated noise is of great significance in the measurement of passive detection parameters. The radiation noise of the ship is a broadband spectrum modulated by the spectral signal envelope. The average azimuth and complex intensifier are used to estimate and analyze the target azimuth, and the estimated angle is statistically processed by histogram and weighted histogram. For the frequency estimation technique, the adaptive frequency estimator based on the adaptive notch filter and the adaptive LMS (Least Mean Square) algorithm is chosen to study the target signal frequency. Through the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, so it is of great significance in the research of underwater acoustic detection technology.

Fast convergent frequency-domain MIMO equalizer for few-mode fiber communication systems

Space division multiplexing using few-mode fibers has been extensively explored to sustain the continuous traffic growth. In few-mode fiber optical systems, both spatial and polarization modes are exploited to transmit parallel channels, thus increasing the overall capacity. However, signals on spatial channels inevitably suffer from the intrinsic inter-modal coupling and large accumulated differential mode group delay (DMGD), which causes spatial modes de-multiplex even harder. Many research articles have demonstrated that frequency domain adaptive multi-input multi-output (MIMO) equalizer can effectively compensate the DMGD and demultiplex the spatial channels with digital signal processing (DSP). However, the large accumulated DMGD usually requires a large number of training blocks for the initial convergence of adaptive MIMO equalizers, which will decrease the overall system efficiency and even degrade the equalizer performance in fast-changing optical channels. Least mean square (LMS) algorithm is always used in MIMO equalization to dynamically demultiplex the spatial signals. We have proposed to use signal power spectral density (PSD) dependent method and noise PSD directed method to improve the convergence speed of adaptive frequency domain LMS algorithm. We also proposed frequency domain recursive least square (RLS) algorithm to further increase the convergence speed of MIMO equalizer at cost of greater hardware complexity. In this paper, we will compare the hardware complexity and convergence speed of signal PSD dependent and noise power directed algorithms against the conventional frequency domain LMS algorithm. In our numerical study of a three-mode 112 Gbit/s PDM-QPSK optical system with 3000 km transmission, the noise PSD directed and signal PSD dependent methods could improve the convergence speed by 48.3% and 36.1% respectively, at cost of 17.2% and 10.7% higher hardware complexity. We will also compare the frequency domain RLS algorithm against conventional frequency domain LMS algorithm. Our numerical study shows that, in a three-mode 224 Gbit/s PDM-16-QAM system with 3000 km transmission, the RLS algorithm could improve the convergence speed by 53.7% over conventional frequency domain LMS algorithm.

Simulation for noise cancellation using LMS adaptive filter

In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

A new step-size searching algorithm based on fuzzy logic and neural networks for LMS adaptive beamforming systems

In this paper, a novel algorithm based on fuzzy logic and neural networks is proposed to find an approximation of the optimal step size $\mu $ for least-mean-squares (LMS) adaptive beamforming systems. A new error ensemble learning (EEL) curve is generated based on the final prediction value of the ensemble-average learning curve of the LMS adaptive algorithm. This information is classified and fed into a back propagation neural network, which automatically generates membership functions for a fuzzy inference system. An estimate of the optimal step size is obtained using a group of linguistic rules and the corresponding defuzzification method. Computer simulations show that a useful approximation of the optimal step size is obtained under different signal-to-noise plus interference ratios. The results are also compared with data obtained from a statistical analysis performed on the EEL curve. As a result of this application, a better mean-square-error is observed during the training process of the adaptive array beamforming system, and a higher directivity is achieved in the radiation beam patterns.

Application of Improved LMS Adaptive Algorithm in Acoustics Pyrometry

Acoustic pyrometry is a comparatively advanced method of temperature measurement developed in recent years, which possesses the essential characteristics of traditional temperature measurement approach. Considering the interferences, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above. In order to make the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses the sliding forgetting-weighted window. The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

Improved LMS Adaptive Algorithm and its Application of Time Delay Estimation in Power Plant

Time delay estimation plays an important role in accuracy of leakage location in industrial boiler furnace. Considering about interference, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above .In order to makes the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses sliding forgetting-weighted window . The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

Signed-gradient adaptive step size LMS algorithm for biomedical applications.

Gradient adaptive step size adaptive filters have been widely used to adapt different biomedical application environments and obtain useful life signals from serious ambient noise and interferences. In order to further improve the signal-to-noise ratio (SNR) of the life signals, this paper presents a class of signed-gradient adaptive step size least mean square (LMS) adaptive filters. The proposed algorithms introduce a sign function to replace the gradient of squared error in the step size updating process of the gradient adaptive step size LMS adaptive filters. The performance of both gradient and signed-gradient algorithms with dual adaptive filters is compared by extracting heartbeat signals from ambient noise in stethoscopes. Simulation results demonstrate that though the signed-gradient adaptive step size LMS algorithm converges at a slower rate at the early stage of iteration, it has a smaller mean squared error (MSE) at the stage of convergence, thus achieves a higher SNR.