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A derivative-free filter algorithm for nonlinear complementarity problem

APPLIED MATHEMATICS AND COMPUTATION 2006年第1期182卷 846-853页

作者： Peng, Yehui Liu, Zhenhai Cent S Univ Sch Math Sci & Comp Technol Changsha 410075 Hunan Peoples R China Huaihua Coll Dept Math Huaihua 418008 Hunan Peoples R China

Recently, so-called derivative-free methods have attracted much attention, which do not require computation of derivatives of function and are particularly suitable for problems which the derivatives are not available or are extremely expensive to compute. This paper presents a new derivative-free algorithm for nonlinear complementarity problem, which makes use of the efficiency of the filter technique. The algorithm is shown, under the monotonicity assumption, to globally converge to the solution of the nonlinear complementarity problem. The numerical results show the algorithm is feasible. (c) 2006 Elsevier Inc. All rights reserved.

关键词： derivative-free method nonlinear complementarity problem filter algorithm global convergence

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Design of Dynamic Calf Weighing System Based on Moving-IIR filter algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第2期16卷 1059-1069页

作者： Zhao, Wenwen Luo, Jinjun Li, Shenglin Qi, Jiangtao Meng, Hewei Li, Yaping Shihezi Univ Coll Elect Engn Shihezi 832000 Peoples R China Minist Agr & Rural Affairs Lab Northwest Agr Equipment Shihezi 832000 Peoples R China First Div Xinjiang Prod & Construct Corps Agr Technol Promot Stn Shihezi 832000 Peoples R China

In order to solve the problems of high intensity, low collecting efficiency, poor information real-time performance and low precision in the calf weighing process, a kind of dynamic calf weighing system was designed. It was based on moving-IIR by establishing a test platform to verify filter algorithm. The software MATLAB was applied to design the moving average filter algorithm, IIR filter algorithm and moving-IIR filter algorithm, respectively, to process and analyze the dynamic data collected in slow, violent and slow-violent states of calves. Test results showed that, the error rates of moving-IIR filter algorithm in slow, violent and slow-violent states of calves were within 1.12%, 0.32% and 2.82%, which were lower than that of moving-average filter algorithm and IIR filter algorithm. In slow state of calves, the moving-IIR filter was not very smooth. In violent and slow-violent states of calves, the standard deviations were within 1.1126 and 1.1520, showing significant smoothness. The study showed that, information collection system based on moving-IIR filter algorithm has fully taken the stability of moving filter and dynamic nature of IIR filter into consideration and had advantages of low error rate and high stability. Therefore, it can realize real time precise collection, display, storage and historical data query of weight information.

关键词： Calves Dynamic weighing filter algorithm System design

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filter algorithm of bayesian state estimation using FFT

Filter algorithm of bayesian state estimation using FFT

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3rd World Congress on Intelligent Control and Automation

作者： Sun, F Wang, SF Xu, YQ Hao, YL Harbin Engn Univ Automat Coll Harbin 150001 Peoples R China

In this paper, a filter algorithm of bayesain state estimation using fast fourier transform(FFT) is proposed. The computation is reduced by using FFT. The simulation result shows that the algorithm is a very robust no... 详细信息

ISBN: (纸本)7312012035

关键词： bayesian state estimation fast fourier transform(FFT) filter algorithm nonlinear stochatis system

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Battery pack SOC estimation by Noise Matrix Self Adjustment-Extended Kalman filter algorithm based on cloud data

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JOURNAL OF ENERGY STORAGE 2024年 84卷

作者： Wang, Limei Gao, Kaixu Han, Jiyan Zhao, Xiuliang Liu, Liang Pan, Chaofeng Li, Guochun Wang, Yun Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Autoparts New Energy Technol Co Ltd Zhenjiang 212132 Peoples R China

Due to the limited computing capacity of the Battery Management System (BMS), the closed-loop estimation method represented by the Kalman filter algorithm is not really widely used, resulting in estimated errors in State of Charge (SOC). Given the strong computing power and large storage capacity of the cloud platform, a SOC estimation algorithm based on cloud data is proposed in the paper. Firstly, the impact of model parameter timevariability on SOC estimation is researched. Subsequently, the adaptability of the Noise Matrix Self AdjustmentExtended Kalman filter (NMSA-EKF) algorithm to the long data transmission period and low data transmission accuracy is discussed. Then, the adaptability of the model parameter identification algorithm is also analyzed. Furthermore, the model parameters are calculated by the combination of the direct method and Variable Forgetting Factor Recursive Least Square (VFFRLS) algorithm. Finally, the NMSA-EKF algorithm is used to estimate the SOC of the cloud-based discharging fragments. The results show that SOC estimation based on the NMSA-EKF algorithm has a high accuracy, and the overall relative error is within 3 %. The results also further validate the accuracy of the proposed parameter identification method.

关键词： Cloud data Parameter identification State of charge filter algorithm Noise Matrix Self Adjustment-Extended Kalman

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A New filter algorithm of Image Based on Fuzzy Logical

A New Filter Algorithm of Image Based on Fuzzy Logical

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2011 International Symposium on Computer Science and Society

作者： ZHANG Hong-qiao MA Xin-jun WU-Ning School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Graduate School

Image denoising by using the filter algorithm is a basic problem which we would meet during the image processing. The mean filter is normally used to reduce Gaussian noise, but it is not effective to the impulse noise. While the median filter is good for reducing the impulse noise but is not well-worked for Gaussian noise. Based on the idea of fuzzy logical, this essay proposes a new kind of filter, the fuzzy filter, which could deal with the two types of noise above to some extent. Finally, according to the experiment data, we have evaluated the performance of the fuzzy filter comparing with the traditional filters and the result is acceptable.

关键词： image denoising fuzzy logic filter algorithm

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Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2025年第1期21卷

作者： Xu, Yanjun Tian, Chunqi Sun, Yaoru Zhang, Haodong Tongji Univ Shanghai Peoples R China

With the growth of web technology, the semantic web offers a promising framework for online knowledge collaboration. However, trust issues can undermine users' willingness to collaborate, reduce the frequency of interaction and collaboration efficiency. This paper introduces a super node-based trust management model designed to enhance semantic networks by linking nodes through trust relationships. The model exploits the synergistic incentives of similar interest behaviours to achieve a steady construction of trust relationships. We propose a similarity filtering algorithm that calculates the similarity to filter out false, misleading, or unfair information effectively. Through simulations, we compare our model with RRGRET, Surework, and community-based approaches, and the results show that our model has good network properties, while also resisting multiple malicious attacks and guaranteeing collaboration success. This research contributes to optimizing node relationships within semantic networks and strengthening network robustness against interference.

关键词： Semantic Network Knowledge Collaborative Trust Management Similar Interests Reputation System filter algorithm

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Mixed variable optimization of a load-bearing thermal insulation system using a filter pattern search algorithm

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OPTIMIZATION AND ENGINEERING 2004年第2期5卷 157-177页

作者： Abramson, MA USAF Inst Technol Dept Math & Stat Wright Patterson AFB OH 45433 USA

This paper describes the optimization of a load-bearing thermal insulation system characterized by hot and cold surfaces with a series of heat intercepts and insulators between them. The optimization problem is represented as a mixed variable programming (MVP) problem with nonlinear constraints, in which the objective is to minimize the power required to maintain the heat intercepts at fixed temperatures so that one surface is kept sufficiently cold. MVP problems are more general than mixed integer nonlinear programming (MINLP) problems in that the discrete variables are categorical;i.e., they must always take on values from a predefined enumerable set or list. Thus, traditional approaches that use branch and bound techniques cannot be applied. In a previous paper, a linearly constrained version of this problem was solved numerically using the Audet-Dennis generalized pattern search (GPS) method for MVP problems. However, this algorithm may not work for problems with general nonlinear constraints. A new algorithm that extends that of Audet and Dennis by incorporating a filter to handle nonlinear constraints makes it possible to solve the more general problem. Additional nonlinear constraints on stress, mass, and thermal contraction are added to that of the previous work in an effort to find a more realistic feasible design. Several computational experiments show a substantial improvement in power required to maintain the system, as compared to the previous literature. The addition of the new constraints leads to a very different design without significantly changing the power required. The results demonstrate that the new algorithm can be applied to a very broad class of optimization problems, for which no previous algorithm with provable convergence results could be applied.

关键词： mixed variable programming categorical variables filter algorithm pattern search algorithm thermal insulation heat intercepts nonlinear constraints

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INTEGRATION OF DIFFERENT filter algorithmS FOR IMPROVING THE GROUND SURFACE EXTRACTION FROM AIRBORNE LIDAR DATA 8

INTEGRATION OF DIFFERENT FILTER ALGORITHMS FOR IMPROVING THE...

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8th International Symposium on Spatial Data Quality

作者： Deng, S. S. Shi, W. Z. HK Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781629935201

An important step for processing airborne Light Detection And Ranging (LiDAR) data is point cloud filtering. Points striking on vegetation and man-made objects and low points (points significantly lower than neighboring points) are filtered out, leaving ground points for generation of digital terrain models (DTM). A variety of filter algorithms have been developed, which have disparate performance in different landscape and environment. This study investigates the potential of integrating the results of different filter algorithms for improving the ground surface extraction from the LiDAR point cloud. A simple procedure was proposed based on a statistical approach to identify and remove filtering errors and combine ground points from each filtering result. The procedure was tested in an area with rugged terrain covered by dense vegetation of variable heights. The filtering results of two popular filter algorithms, progressive TIN (Triangulated Irregular Network) densification and hierarchical robust interpolation, were integrated. The filtering results of two algorithms and the integration result were qualitatively evaluated. The evaluation results indicated that the proposed integration procedure can remove most vegetation points that were not filtered out by filter algorithms, and combine ground points from each filtering result.

关键词： Airborne LiDAR filter algorithm Integration filtering error Statistics

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Comparative study of a new semi-empirical model of the proton exchange membrane fuel cell for online prognostics applications

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ENERGY CONVERSION AND MANAGEMENT 2025年 331卷

作者： Perez, L. M. Jemei, Samir Boulon, Loic Ravey, Alexandre Kandidayeni, Mohsen Solano, Javier Univ Franche Comte FEMTO ST Inst CNRS FCLAB F-90000 Belfort France Univ Quebec Trois Rivieres Dept Elect & Comp Engn Hydrogen Res Inst Trois Rivieres PQ G8Z 4M3 Canada UTBM CNRS Inst FEMTO ST FCLAB F-90000 Belfort France European Inst Energy Res EIFER Emmy Noether Str 11 D-76131 Karlsruhe Germany

The prognostic of the proton exchange membrane fuel cell is a current topic of research. Consequently, the complexity of its degradation mechanisms has led to the development of semi-empirical models to improve predictive analysis. The accurate estimation of parameters for these models is a challenging task due to their multivariate, nonlinear, and complex characteristics. This work proposes anew semi-empirical model of the proton exchange membrane fuel cell and compares it with a widely used model in the literature. Unlike other similar studies, this comparison not only focuses on minimizing the sum of squared errors in relation to the experimental data but also evaluates the variation in the solution set and the computational effort involved. For both models, the unknown parameters are estimated using the recent Pelican Optimization algorithm. Four datasets are used to evaluate the development of the proposed model and the selected benchmark model. The first three datasets are open-access and well-recognized in academic literature, whereas the fourth dataset was obtained from a developed experimental test bench. The results show that the proposed model achieves high accuracy, with a mean absolute percentage error lower than 0.89% and the sum of squared errors below 0.9272 for all the studied scenarios. This model reduces parameter variation and decreases the relative standard deviation by over 12.7% compared to the utilized benchmark model for the first three datasets. Hence, the proposed model not only improves the precision of the estimated parameters without a notable increase in error but also reduces the computational load by at least 21.7% across all case studies.

关键词： Proton exchange membrane fuel cell Parameter estimation Pelican optimization algorithm Model identification Prognostic and degradation filter algorithm

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An Improved algorithm of Harmonic Mean filter

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Journal of Systems Science and Information 2006年第4期4卷 697-703页

作者： Xiaoming Zhao Xijian Ye Shenzhou Zheng Department of Computer Taizhou University Taizhou 317000 China School of Science Beijing Jiaotong University Beijing 100044 China

Rafael C. Gnzalez has mentioned an algorithm on adaptive local noise elimination filter in the book named Digital Image Processing. This paper points out the algorithm＇s deficiency and presents an improved harmonic mean filter algorithm which makes mean square error emse cutting quarter but SNR, SNPm and PSNR increasing a tenth more than original algorithm. This filter algorithm is verified to be effective by simulation experiment.

关键词： filter algorithm harmonic mean filter algorithm noise elimination filter

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