Variable Step-size Algorithm Research Articles

Piezoelectric-based smart structures for aero-engine blade: Piezoelectric layout optimization and vibration suppression tests

To promote the use of piezoelectric smart structures in aero-engine blades, this paper introduces a novel method for optimizing the layout of piezoelectric elements on the blade's surface and an active vibration control method, along with vibration suppression tests. A preprocessing method is introduced for the point cloud data of in-service blades to reconstruct the blade surface. An improved parameter-adaptive differential evolution algorithm is proposed to optimize the layout of piezoelectric actuators and sensors and accurately identify the hysteresis nonlinear model of piezoelectric elements. A Hammerstein model is constructed by cascading an asymmetric Bouc-Wen model with an ARX model to describe the rate-dependent hysteresis nonlinear characteristics of piezoelectric elements. An improved variable step-size FxLMS algorithm is proposed to balance the relationship between convergence speed and steady-state error in the control algorithm. The proposed layout optimization and vibration control algorithm are validated experimentally on an experimental platform under various disturbances, with results showing improved performance of the proposed VSS-FxLMS algorithm in terms of convergence and efficiency.

Geometric algebra and cosine-function based variable step-size adaptive filtering algorithms

Geometric algebra and cosine-function based variable step-size adaptive filtering algorithms

Mining Technology Evaluation for Steep Coal Seams Based on a GA-BP Neural Network.

Many mines in Guizhou Province are in urgent need of renovation to ensure harmonious operation and prolong their lifespan. The key to successful renovation lies in the prudent selection of the appropriate mining technologies. Therefore, a comprehensive investigation was conducted on steep coal mines in Guizhou Province, and a comprehensive evaluation framework was established. Spearman correlation analysis was performed on various factors, selecting geological conditions and working face parameters with high correlation as the input variables and mining methods as the output variables. The optimal values of each hyperparameter were determined through orthogonal experiments, and the neural network structure was confirmed to be "17-9-3". Five variants of backpropagation (BP) algorithms were meticulously tested, and a genetic algorithm optimizing the BP neural network (GA-BP) was further assessed to improve the model's prediction accuracy. The accuracy of the model was evaluated via the coefficient of determination (R 2) and mean squared error (MSE). The research results indicated that the variable step-size algorithm with a momentum term (VSS + MT) was the optimal algorithm for the BP neural network. Additionally, the MSE values of the artificial neural network and GA-BP neural network in the testing phase were 0.06 and 0.04, with prediction success rates of 70 and 90%, respectively, and R 2 values of 0.79 and 0.85, respectively. Thus, the GA-BP neural network demonstrated superior performance. Finally, industrial application of the model was conducted on a working face in the Zhong-Yu coal mine. The evaluation index for the working face was "0.847, 0.09, 0.111", suggesting that fully mechanized mining should be adopted. The evaluation results were consistent with the current production status of the mine, verifying the reliability of the model in practical applications.

A novel approach to geometric algebra-based variable step-size LMS adaptive filtering algorithm

A novel approach to geometric algebra-based variable step-size LMS adaptive filtering algorithm

Research on Multibeam Bathymetric System Based on Greedy Algorithm and Variable Step Size Traversal Algorithm

Multi-beam bathymetry system is an advanced technology to measure the seabed based on single-beam bathymetry. In this paper, the greedy algorithm and variable step traversal method will be used to establish the mathematical models of coverage width and overlapping rate of adjacent strips of multi-beam sounding, and the best scheme of survey line layout will be designed by establishing the optimization model. According to the established mathematical model of overlap rate, the mathematical model of coverage width of multi-beam sounding in three-dimensional rectangular waters is optimized. Finally, according to the real life, with the overlapping rate of adjacent strips within 10% to 20% as the limiting condition and the minimum total length of survey lines as the objective function, a single-objective optimization model is established. Then, Python is used to solve the problem through greedy algorithm and variable step-size traversal algorithm, and it is found that the included angle of survey line direction in the optimal scheme is 90, and the minimum total length of survey line is 68 nautical miles. Stability analysis is carried out by changing the constraint range of overlap rate.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Machine vision-based automatic focusing method for robot laser welding system

Background Defocus distance is a critical parameter in laser welding, especially when encountering changes in the contour of the welding surface. This paper proposed an automated focusing method to address the challenging issue of accurately adjusting the defocus distance. Methods The proposed method involves several steps. Firstly, a clarity evaluation function based on the Kirsch operator is employed to calculate real-time image clarity of the welding surface captured by the machine vision system. Next, an improved Canny edge detection algorithm is applied to identify the edge contours of the welding surface, from which their central points are extracted. Finally, automatic focusing is achieved by employing a variable step-size hill-climbing algorithm to search for the focal plane. Results To verify the applicability of the automatic focusing method proposed for welding the solder ring, a robot laser welding system was designed and constructed. Experimental results show that the positioning error of the robot after automatic focusing is within ±0.4 mm. The average time required for a single automatic focusing process is 16.27 s. These results demonstrated the successful accomplishment of automatic adjustment and control of the focal length. Conclusions The machine vision-based automatic focusing method proposed in this paper enhances the consistency of the robot’s position after automatic focusing in robot laser welding systems. It elevates the level of automation in the welding process and provides an efficient solution for accurately adjusting the welding focal distance during the laser welding process.

Comparison of the performance of artificial neural network with variable step-size adaptive algorithms for the beamforming of smart antenna for cellular networks

A smart antenna is an antenna array that uses spatial diversity to identify the desired mobile station (MS) and reject the unwanted interference signal in a cellular network. Generally, adaptive signal processing algorithms are used for smart antenna beamforming, and one of the most common algorithms is the least mean square (LMS) algorithm. Here, the artificial neural network (ANN) is used for beamforming of smart antennas, and the performance of the ANN is compared with the performance of variable step-size LMS (VS-LMS) and variable step-size sign LMS (VS-SLMS) algorithms. The ANN has better performance than the VS-LMS and VS-SLMS algorithms for the determination of user and null directions. Lower side lobe levels (SLLs) are achieved using ANN compared to the VS-LMS and VS-SLMS algorithms. The reduction of SLL from about 3.5 dB to 8.5 dB is achieved using ANN compared to signal processing algorithms.

An adaptive algorithm for acoustic feedback compensation and secondary path identification of an active noise control system.

An adaptive variable step-size algorithm is proposed in this paper to address the impact of the real-time acoustic feedback and the real-time secondary path identification on the overall noise reduction performance of an active noise control system. An automated adjustment weight factor is introduced in the algorithm to accelerate the convergence of the acoustic feedback path as well as the secondary path identification, and to prevent possible system divergence. It is shown in this study that the proposed algorithm can resolve the trade-off between a fast convergence and a low misalignment of the virtual and the actual control paths typically found in conventional algorithms. An optimized control structure is also proposed in the study by enabling an adaptive gain adjustment based on the output of the auxiliary filter to enhance the practicality of the control system. The effectiveness of the algorithm is tested using two simulated multi-component signals and a broadband noise signal, and the results confirm that the proposed algorithm can achieve a good noise reduction with only a few iterations.

Steady state oscillations reduction using neural network IC-based variable step Size MPPT

This paper deals with the development of neural network IC-based variable step size MPPT controller. The proposed neural network MPPT controller is firstly, developed in offline mode required for testing different set of neural network parameters and architectures, and used secondly in the online mode to track the output power of the PV system composed of Solarex MSX 60W PV module fed by a DC-DC boost converter drived using the proposed ANN MPPT controller. The proposed neural network MPPT controller is tested and validated using Matlab/Simulink environments. Simulation results and analysis are presented showing good performances for the proposed controller compared to the conventional fixed or variable step size algorithms.

Self-Adaptive PDLC Control Strategy With Smart Light Intensity Adjustment Using Photovoltaic-Thermoelectric Hybrid Energy Supply Technology

As an electronically controlled glass whose transmittance varies with voltage, polymer dispersed liquid crystal (PDLC) has the advantages of good privacy, high transmittance, fast response speed, and good heat shielding performance. It can be applied to electronically controlled building windows. However, PDLC needs to consume extra power, and electronically controlled PDLC has not yet been integrated with intelligent home environment control. In this paper, we proposed an adaptive variable voltage step-size fast control algorithm in Field Programmable Gate Array (FPGA)-based home environment control system, which is powered by photovoltaic-thermoelectric (PV-TEG) hybrid sources. The power supply voltage of the PDLC can be dynamically changed through the adaptive Pulse width modulation (PWM) duty cycle adjustment technology to realize intelligent transmittance control, thereby improving the indoor lighting environment. Experimental results show that by applying the proposed algorithm, the indoor temperature is reduced by 2°C, and the adaptive and stable regulation of the optimal indoor illumination of 500 Lux can be completed in less than 2.5 seconds. This paper provides a feasible solution for the low-energy intelligent integrated control and monitoring of the modern home environment.

Cosine function variable step-size transform domain least mean square algorithm based on matrix rotation

A novel cosine function variable step-size transform domain least mean square (LMS) algorithm based on matrix rotation (COS-ROTDCT-LMS-DFE) was put forward. Based on the discrete cosine transform domain LMS (DCT-LMS) algorithm, it was first processed by matrix rotation in an anticlockwise direction to resolve the problem of self-interference between signals, which effectively sped up the convergence of the algorithm. Subsequently, a cosine function variable step-size algorithm was developed considering that its curve was characterized by a smooth top and fast rising and falling speed so as to further improve the convergence speed of the algorithm. Subsequently, the decision feedback equalizer (DFE) was introduced into the algorithm to compensate signal distortion and noise. The convergence performance of the algorithm was simulated in typical multipath underwater acoustic channels. The simulation results showed that the convergence performance of the new algorithm was equivalent to that of recursive least squares (RLS). The convergence speed of COS-ROTDCT-LMS-DFE was even better than that of the recursive least square decision feedback equalizer (RLS-DFE) algorithm. The new algorithm performed well in terms of both convergence speed and steady-state error and could be easily implemented.

A variable step size least mean p-power adaptive filtering algorithm based on multi-moment error fusion

For the channel estimation problem under α-stable distributed impulse interference, the traditional fixed-step adaptive filtering cannot satisfy the fast convergence speed and low steady-state error at the same time, whereas the variable-step method is able to effectively solve this contradiction. This paper proposes an improved variable step-size least mean p-power adaptive algorithm that offers good robustness against impulsive noise. The proposed algorithm takes into account the linkage between the errors and uses the adjustment of the step size based on the errors of the current moment and the previous k moments, thus overcoming the problems of poor anti-noise performance and large steady-state fluctuations of the fixed-step size algorithm. This algorithm ensures that the step size does not change abruptly when the system is disturbed by impulse noise and can achieve a lower steady-state error. The simulation results show that the algorithm has better convergence than the traditional fixed step-size algorithm and the existing variable step-size algorithm under the interference of impulsive noise.

Combined boosted variable step-size affine projection sign algorithm for environments with impulsive noise

The affine projection sign algorithm (APSA) is widely used to alleviate the impact of impulsive noise on weight updates for adaptive filters. When using the conventional APSA, the sign-based approach tends to degrade the rate of convergence; however, this can be remedied by adopting a variable step-size (VSS). Nonetheless, the tracking performance of the APSA tends to be poor in situations that involve identifying rapid changes in the system model. Inspired by the boosted adaptive filtering framework, we propose using multiple component filters (weak learners) to calculate the local boosted VSS. Specifically, this involves combining the boosted VSSs of the first and last stages of the component filters. In simulations, the proposed combined boosted VSS (CBVSS) method was shown to outperform comparable works in terms of convergence rate, even in situations where the unknown system model changes abruptly.

Variable Step Size Methods of the Hybrid Affine Projection Adaptive Filtering Algorithm under Symmetrical Non-Gaussian Noise

The idea of variable step-size was introduced into the Hybrid Affine Projection Algorithm (H-APA) and we propose two variable step size algorithms based on H-APA, which are called the Variable Step-Size Hybrid Affine Projection Algorithm (VSS-H-APA) and the Modified Variable Step-Size Hybrid Affine Projection Algorithm (MVSS-H-APA). These are two variable-step algorithms aim to further improve the robust performance and convergence speed of H-APA under non-Gaussian noise. This allows for faster convergence while maintaining stability. The MVSS-H-APA goes further than VSS-H-APA to estimate the noise in order to achieve better convergence performance. The proposed algorithm performs better than the existing algorithms in system identification under symmetric non-Gaussian noise.

New variable step-size fast NLMS algorithm for non-stationary systems

New variable step-size fast NLMS algorithm for non-stationary systems

Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter

Acoustic feedback is a common phenomenon that occurs during hearing aid use, limiting the maximum gain that a hearing aid can provide. Effective cancellation of acoustic feedback is an essential feature of hearing aids. However, due to the complex environments in which hearing aids are used and the frequently changing acoustic feedback path, it is difficult for existing adaptive filter-based acoustic feedback cancellation algorithms to balance both convergence speed and steady-state error. For this reason, based on the nonparametric variable step size (NPVSS) algorithm, a weighted NPVSS algorithm that also introduces a prediction error method is proposed in this paper. First, by introducing the prediction error method, the adaptive filter bias caused by the nonwhite source signal is effectively reduced. Second, the proposed weighting mechanism weights the error signal according to the adaptive filter misalignment, which enhances the steady-state robustness of the algorithm while accelerating its convergence. In addition, a new low-complexity method is herein proposed for source signal energy estimation by reusing the misalignment information to solve the step size calculation problem of the NPVSS algorithm. Simulation results show that the new algorithm exhibits greater robustness and faster convergence than similar algorithms. The proposed algorithm is implemented with a real hearing aid and its performance is measured on a dummy head in a soundproof room. The test results demonstrate that the proposed algorithm achieves a 35% reduction in convergence time compared with PEM-IMLMS and a 60% reduction compared with PEM-NLMS. Furthermore, the proposed algorithm reduces the sound pressure level of acoustic feedback residues compared with PEM-IMLMS and PEM-NLMS by approximately 2 dB SPL and 6 dB SPL, respectively. These results indicate that the new algorithm can provide timely and stable cancellation of acoustic feedback.

Zero-Drift Error Compensation Method for Electronic Balance Based on Adaptive Filter With Autostep LMS

This article presents a new approach of compensating the zero-drift error of electronic balance based on automatic step adjustment and least mean square method (Autostep-LMS), and the detailed algorithm of Autostep-LMS is given. Unlike the classical variable step-size LMS algorithms, such as Aboulnasr-LMS algorithm, Versoria-function-LMS algorithm, and Gradient-LMS algorithm, the step control parameters of Autostep-LMS algorithm are automatically adjusted, which is helpful to reduce the complexity of how to adjust these algorithm parameters. By comparing with these classical variable step-size LMS algorithms, the simulation results show that the Autostep-LMS algorithm has faster convergence speed and smaller steady-state error. This Autostep-LMS algorithm is applied to the electronic balance, which range is 200 g and the resolution is 1 mg, and the experimental results show that the zero-drift error, repeatability error, and nonlinear error of the electronic balance are all less than 5 mg, which is superior to the recommendation of the class II electronic balance.

Efficient Industrial Robot Calibration via a Novel Unscented Kalman Filter-Incorporated Variable Step-Size Levenberg–Marquardt Algorithm

Robots facilitate a critical category of equipments to implement intelligent production. However, due to extensively inevitable factors like structural errors and gear tolerances, the positioning error of an industrial robot is several millimeters, therefore failing in fulfilling the high-precision manufacture requirements. To address the critical problem, this work develops a novel calibration algorithm incorporates an Unscented Kalman Filter and a Variable Step-size Levenberg-Marquardt (UKF-VSLM) algorithm for efficient industrial robot calibration with the following two-fold ideas: a) developing a novel Variable Step-size Levenberg-Marquardt algorithm to address the local optimum issues encountered by a standard Levenberg-Marquardt algorithm; and b) incorporating an unscented Kalman filter into the proposed Variable Step-size Levenberg-Marquardt algorithm to suppressing the measurement noises during the calibration process. Empirical studies on an HSR JR680 industrial robot demonstrate that compared with state-of-the-art calibration algorithms, the calibration accuracy of the developed UKF-VSLM is 19.51% higher than that of the most accurate Levenberg-Marquardt algorithm measured by the maximum error. The empirical results strongly support the superior performance of the proposed algorithm in addressing robot calibration issues.

Pedestrian mobility management for heterogeneous networks

<p>Pending the arrival of the next generation of 5G which is not yet deployed in<br />some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction<br />process is performed by the VSS-NLMS algorithm, an adaptive filter<br />known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.</p>