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Adaptive Controller for Vehicle Active Suspension Generated Through LMS Filter Algorithms 认领 引用 被引量：2
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作者 孙建民 舒歌群 《Transactions of Tianjin University》 EI CAS 2006年第3期163-168,共6页
The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freed... The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system. 展开更多
关键词 adaptive controller LMS filter algorithms riding comfort handling safety
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Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm 认领 引用
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作者 王伟峰 YANG Bo +1 位作者 LIU Hanfei QIN Xuebin 《High Technology Letters》 EI CAS 2023年第2期113-121,共9页
Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific... Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value. 展开更多
关键词 vehicle recognition target tracking annealing chaotic particle swarm Gauss particle filter(GPF)algorithm
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Short-term Wind Power Forecasting Using Interval A2-C1 Type-2 TSK FLS Method with Extended Kalman Filter Algorithm 认领 引用
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作者 Jun Li Mingdi Miao 《Chinese Journal of Electrical Engineering》 EI CSCD 2025年第3期191-215,共25页
For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algo... For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algorithm is proposed.Compared with the type-1(T1)FLS model,the IT2 TSK FLS method can simultaneously model both intra-and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further.The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region,under the same conditions.It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation(BP)and particle swarm optimization(PSO)algorithms,as well as IT2 A2-C0 TSK FLS methods with EKF.The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance,which demonstrates its effectiveness and application potential. 展开更多
关键词 Wind power forecasting interval type-2 TSK fuzzy logic system extended Kalman filter(EKF)algorithm A2-C1
Stability analysis of distributed Kalman filtering algorithm for stochastic regression model 认领 引用
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作者 Siyu Xie Die Gan Zhixin Liu 《Control Theory and Technology》 EI CSCD 2025年第2期161-175,共15页
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi... The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example. 展开更多
关键词 Distributed Kalman filtering algorithm Stochastic cooperative information condition Sensor networks (Lp)-exponential stability Stochastic regression model
Variable Projection Order Adaptive Filtering Algorithm for Self-interference Cancellation in Airborne Radars 认领 引用
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作者 LI Haorui GAO Ying +1 位作者 GUO Xinyu OU Shifeng 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第4期497-508,共12页
The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in... The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference. 展开更多
关键词 adaptive filtering algorithm airborne radar variable projection order mean squared deviation self-interference cancellation
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Filtering algorithm of line structured light for long-distance obstacle detection 认领 引用
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作者 邵海燕 张振海 李科杰 《Journal of Beijing Institute of Technology》 EI CAS 2016年第4期521-525,共5页
Since unmanned ground vehicles often encounter concave and convex obstacles in wild ground, a filtering algorithm using line structured light to detect these long distance obstacles is proposed. For the line structure... Since unmanned ground vehicles often encounter concave and convex obstacles in wild ground, a filtering algorithm using line structured light to detect these long distance obstacles is proposed. For the line structured light image, a ranked-order based adaptively extremum median （RAEM） filter algorithm on salt and pepper noise is presented. In the algorithm, firstly effective points and noise points in a filtering window are differentiated; then the gray values of noise points are replaced by the medium of gray values of the effective pixels, with the efficient points＇ gray values unchanged; in the end this algorithm is proved to be efficient by experiments. Experimental resuits demonstrate that the image blur, resulting into proposed algorithm can remove noise points effectively and minimize the protecting the edge information as much as possible. 展开更多
关键词 unmanned ground vehicles line structured light concave and convex obstacles detec-tion ranked-order based adaptively extremum median （RAEM） filter filter algorithm
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Stability and performance analysis of the compressed Kalman filter algorithm for sparse stochastic systems 认领 引用 被引量：2
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作者 LI RongJiang GAN Die +1 位作者 XIE SiYu LüJinHu 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2024年第2期380-394,共15页
This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propos... This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals. 展开更多
关键词 sparse signal compressed sensing Kalman filter algorithm compressed excitation condition stochastic stability tracking performance
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NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM 认领 引用 被引量：4
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作者 ZHANG Qin TAO Ben-zao +1 位作者 ZHAO Chao-ying WANG Li 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2005年第2期170-176,共7页
Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-p... Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-posed.The deviation in the estimation cannot be avoided.Furthermore,the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions.To solve the above problems in GPS dynamic positioning by using EKF,a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American.The method separates the spatial parts from temporal parts during processing the GPS filter problems,and solves the nonlinear GPS dynamic positioning,thus getting stable and reliable dynamic positioning solutions. 展开更多
关键词 GPS dynamic positioning Bancroft algorithm extended Kalman filter algorithm
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Amplitude phase control for electro-hydraulic servo system based on normalized least-mean-square adaptive filtering algorithm 认领 引用 被引量：6
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作者 姚建均 富威 +1 位作者 胡胜海 韩俊伟 《Journal of Central South University》 SCIE EI CAS 2011年第3期755-759,共5页
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit... The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision. 展开更多
关键词 amplitude attenuation phase delay normalized least-mean-square adaptive filtering algorithm tracking performance electro- hydraulic servo system
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Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm 认领 引用 被引量：4
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作者 Guangbo Xu Bingting Zha +2 位作者 Hailu Yuan Zhen Zheng He Zhang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第7期1-13,共13页
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ... The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance. 展开更多
关键词 Laser fuze Underwater laser detection Backscatter adaptive filter Spline least mean square algorithm Nonlinear filtering algorithm
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Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition 认领 引用 被引量：4
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作者 Wentao Ma Jiaohua Qin +3 位作者 Xuyu Xiang Yun Tan Yuanjing Luo Neal NXiong 《Computers, Materials & Continua》 SCIE EI 2019年第3期665-677,共13页
As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and ... As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications. 展开更多
关键词 Image preprocessing machine learning CAPTCHA recognition adaptive median filtering algorithm.
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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 认领 引用 被引量：3
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作者 YAO Yu ZHU Shanfeng CHEN Xinmeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1086-1090,共5页
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider... In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage. 展开更多
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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Research on Kalman Filtering Algorithmfor Deformation Information Series ofSimilar Single-Difference Model 认领 引用 被引量：15
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作者 吕伟才 徐绍铨 《Journal of China University of Mining and Technology》 2004年第2期189-194,199,共6页
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcomin... Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series. 展开更多
关键词 similar single-difference methodology GPS deformation monitoring single epoch deformation information series Kalman filtering algorithm
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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter 认领 引用 被引量：8
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作者 NIE Yongfang ZHANG Tao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第2期229-235,共7页
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ... With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones. 展开更多
关键词 Gaussian mixture probability hypothesis density(GM-PHD) filter pruning algorithm proximity targets clutter rate
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Improved particle filtering techniques based on generalized interactive genetic algorithm 认领 引用 被引量：5
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作者 Yan Zhang Shafei Wang Jicheng Li 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第1期242-250,共9页
This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this pa... This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF. 展开更多
关键词 particle filtering（PF） particle degeneration particle shortage broad interactive genetic algorithm
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A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering 认领 引用 被引量：5
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作者 Xingsheng Deng Guo Tang Qingyang Wang 《Geodesy and Geodynamics》 EI CSCD 2022年第1期38-49,共12页
Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in... Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing(ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10%.The results show that the proposed method is feasible and can be used in different terrains. 展开更多
关键词 Small grid density clustering DBSCAN Fast classification filtering algorithm
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Weighted adaptive filtering algorithm for carrier tracking of deep space signal 认领 引用 被引量：9
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作者 Song Qingping Liu Rongke 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2015年第4期1236-1244,共9页
Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is aut... Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect. 展开更多
关键词 Adaptive algorithms Carrier tracking Deep space communicationKalman filters Tracking accuracy Weighted
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TEC and Instrumental Bias Estimation of GAGAN Station Using Kalman Filter and SCORE Algorithm 认领 引用 被引量：2
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作者 Dhiraj Sunehra 《Positioning》 2016年第1期41-50,共10页
The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of ... The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented. 展开更多
关键词 GPS Aided Geo Augmented Navigation Total Electron Content Instrumental Biases Kalman Filter Score Algorithm
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Design and Implementation of Collaborative Filtering Recommendation Algorithm for Multi-layer Networks 认领 引用
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作者 Ling Gou Lin Zhou Yuzhi Xiao 《国际计算机前沿大会会议论文集》 EI 2021年第1期32-50,共19页
With the continuous development of mobile communications and Internet technologies,the marketing model of the communications industry has shifted from calling-based to social APP-based personalized recommendations.In ... With the continuous development of mobile communications and Internet technologies,the marketing model of the communications industry has shifted from calling-based to social APP-based personalized recommendations.In order to improve the accuracy of recommendation,this paper proposes a recommendation algorithm for social analysis.Empirical data was firstly used to construct a“user-APP”two-layer communication network model,and then the traditional collaborative filtering recommendation technology was integrated to reconstruct similar users and similar APP network model.The bipartite graph weight distribution method was taken to recommend targets in the obtained network model.The experimental simulation shows that,in view of the characteristics of the twolayer communication network,compared with the traditional recommendation algorithm,the algorithm effectively improves the accuracy of the score prediction. 展开更多
关键词 Two-layer communication network Social network analysis Recommendation algorithm Collaborative filtering algorithm
Study on the Heart Sound Signal Denoising Technology based on Integrated Filtering Algorithm 认领 引用
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《International English Education Research》 2013年第12期93-95,共3页
In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise ... In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated faltering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved. 展开更多
关键词 Integrated Filtering Algorithm Heart Sounds Denoising Technology Filtering Algorithm
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