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Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm 认领 引用 被引量：2
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作者 毛力 宋益春 +2 位作者 李引 杨弘 肖炜 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期51-55,共5页
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC... For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly. 展开更多
关键词 fuzzy c-means(FCM) particle swarm optimization(PSO) clustering algorithm new metric norm
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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance 认领 引用 被引量：4
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作者 Shaochun PANG Yijie +1 位作者 SHAO Sen JIANG Keyuan 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第5期636-642,共7页
This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of ... This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%. 展开更多
关键词 objective function clustering center fuzzy C-means （FCM） clustering algorithm degree of member-ship
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Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization 认领 引用 被引量：1
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作者 赵小强 周金虎 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第2期164-170,共7页
Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some ... Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm. 展开更多
关键词 data mining clustering algorithm possibilistic fuzzy c-means(PFCM) kernel possibilistic fuzzy c-means algorithm based on invasiv
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A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm 认领 引用 被引量：3
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作者 Jiulun Fan Jing Li 《Applied Mathematics》 2014年第8期1275-1283,共9页
Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorit... Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm. 展开更多
关键词 Hard C-Means Clustering Algorithm Fuzzy C-Means Clustering Algorithm Suppressed Fuzzy C-Means Clustering Algorithm Suppressed Rate
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Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm 认领 引用
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作者 Krishnamoorthi Murugasamy Kalamani Murugasamy 《Circuits and Systems》 2016年第9期2339-2348,共10页
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis... Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm. 展开更多
关键词 Clustering Optimization K-Means Fuzzy C-Means Firefly Algorithm F-Firefly
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Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection 认领 引用 被引量：2
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作者 Yanhui Xu Yihao Gao +4 位作者 Yundan Cheng Yuhang Sun Xuesong Li Xianxian Pan Hao Yu 《Global Energy Interconnection》 EI CSCD 2023年第4期505-516,共12页
The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection an... The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution. 展开更多
关键词 Load substation clustering Simulated annealing genetic algorithm Kernel fuzzy C-means algorithm Clustering evaluation
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Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 姜文英 林焰 +1 位作者 陈明 于雁云 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期565-570,共6页
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car... Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach. 展开更多
关键词 cargo oil tank optimization design nonlinear programming improved particle swarm optimization（PSO）algorithm fuzzy constraint construction feasibility degree
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A weighted fuzzy C-means clustering method for hardness prediction 认领 引用
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作者 Yuan Liu Shi-zhong Wei 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第1期176-191,共16页
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d... The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model. 展开更多
关键词 Hardness prediction Weighted fuzzy C-means algorithm Feature selection Particle swarm optimization Support vector regression Dispersion reduction
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MATHEMATICAL ANALYSIS OF MUTATION OPERATOR AND ITS IMPROVED STRATEGY IN GENETIC ALGORITHMS 认领 引用
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作者 Zhang Liangjie Mao Zhihong Li Yanda 《Journal of Electronics(China)》 1997年第2期154-158,共5页
This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mut... This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples. 展开更多
关键词 Genetic algorithm(GA) i-bit improved sub-space Fuzzy reasoning
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Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm 认领 引用
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作者 Hanane Barrah Abdeljabbar Cherkaoui Driss Sarsri 《Journal of Computer and Communications》 2016年第10期13-21,共9页
In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many research... In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints. 展开更多
关键词 Agents and MAS MR Images Fuzzy Clustering C-Means Algorithm Image Segmentation
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The Application of Improved Fuzzy Comprehensive Evaluation method in Practice Teaching Quality Evaluation System 认领 引用
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作者 Hu Yang Su Lin 《International Journal of Technology Management》 2013年第8期60-64,共5页
According to the theory of fuzzy mathematics, Fuzzy comprehensive evaluation method of the original algorithm is improved, and reduced the possibility loss of the original evaluation data. The improved algorithm is ap... According to the theory of fuzzy mathematics, Fuzzy comprehensive evaluation method of the original algorithm is improved, and reduced the possibility loss of the original evaluation data. The improved algorithm is applied in the practice teaching quality evaluation system, and improved the practice teaching quality evaluation results and the accuracy of visual, and promote the teaching management scientific, standardized and institutionalized. In order to establish incentive mechanism, it can bring a positive role to improve teaching quality. 展开更多
关键词 Fuzzy comprehensive evaluation Improved algorithm Quality of practice teaching Evaluation system
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Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm 认领 引用 被引量：1
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作者 WANG Jing TANG Jilong +3 位作者 LIU Jibin REN Chunying LIU Xiangnan FENG Jiang 《Chinese Geographical Science》 SCIE 2009年第1期83-88,共6页
Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich textur... Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM. 展开更多
关键词 Adaptive Genetic Algorithm （AGA） Alternative Fuzzy C-Means （AFCM） image segmentation remote sensing
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基于改进型蜣螂算法Fuzzy-Smith-LADRC混凝投药 认领 引用 被引量：2
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作者 王文成 余智科 郑诗翰 《电子测量技术》 北大核心 2025年第3期10-17,共8页
二十届三中全会强调全面落实深化改革水利任务,其中居民饮用水是重点民生任务,混凝工艺是饮用水处理的关键环节。由于混凝过程具有大时滞特性,故对于原水水质频繁变化的控制系统,常规的PID控制不能达到满意的效果。为此,将一种不依赖系... 二十届三中全会强调全面落实深化改革水利任务,其中居民饮用水是重点民生任务,混凝工艺是饮用水处理的关键环节。由于混凝过程具有大时滞特性,故对于原水水质频繁变化的控制系统,常规的PID控制不能达到满意的效果。为此,将一种不依赖系统精确模型的线性自抗扰控制器(LADRC)应用于系统中,利用扩张观测器对混凝控制系统中出现的扰动进行估计并补偿,同时设计史密斯预估器(Smith)与模糊控制器(Fuzzy)相结合的自适应史密斯控制器来消除大时滞对控制效果的影响,提出Fuzzy-Smith-LADRC控制器。针对控制器参数调节困难而引入改进型蜣螂算法(MSIDBO)进行参数整定。改进型算法对DBO算法中初始种群分布不均匀、易陷入局部最优解等问题进行优化,使得MSIDBO能快速收敛并更好平衡全局探索与局部开发能力。系统模型精确时,该控制方法比PID控制的调节时间减少279 s和超调量降低8%,比DMC控制的调节时间减少40 s,系统模型变化时,相比LADRC具有更好的抗干扰性与鲁棒性。 展开更多
关键词 混凝工艺 模糊史密斯预估-线性自抗扰 改进蜣螂算法 参数优化
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Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect Intrusion in Various Datasets 认领 引用 被引量：2
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作者 Ouail Mjahed Salah El Hadaj +1 位作者 El Mahdi El Guarmah Soukaina Mjahed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期265-298,共34页
Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervise... Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness. 展开更多
关键词 Classification neural networks Fuzzy C-means metaheuristic algorithm CICIDS2017 intrusion detection system
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Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing 认领 引用
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作者 P.Rahul N.Kanthimathi +1 位作者 B.Kaarthick M.Leeban Moses 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1583-1600,共18页
Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of th... Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc. 展开更多
关键词 Ad-hoc load balancing H-MANET fuzzy logic system genetic algorithm node quality-based clustering algorithm improved weighted clustering fruitfly optimization
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A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY 认领 引用 被引量：2
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作者 Fu Yusheng Xie Yan Pi Yiming Hou Yinming 《Journal of Electronics(China)》 2006年第4期598-601,共4页
In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage o... In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data. 展开更多
关键词 Radar polarimetry Synthetic Aperture Radar （SAR） Fuzzy set theory Unsupervised classification Image quantization Image enhancement Fuzzy C-Means （FCM） clustering algorithm Membership function
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Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering 认领 引用 被引量：1
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作者 Hongwei Li Lilong Dou +3 位作者 Shuaibing Li Yongqiang Kang Xingzu Yang Haiying Dong 《Chinese Journal of Electrical Engineering》 CSCD 2023年第1期129-141,共13页
An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method f... An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed.First,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC.Then,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum features.Finally,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact.An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method.The EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts.The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC. 展开更多
关键词 On-load tap changer singular spectrum analysis Hilbert-Huang transform gray wolf optimization algorithm fuzzy C-means clustering
基于改进A*的模糊PID煤矿巡检机器人路径规划 认领 引用 被引量：1
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作者 金智新 罗实 +2 位作者 李永安 梁威 李佳浩 《电子测量技术》 北大核心 2026年第5期40-51,共12页
为应对煤矿巡检机器人在复杂、动态的井下巷道环境中进行路径规划时面临的安全性不足和跟踪精度差的挑战,本文提出了一种融合改进A*全局规划与模糊PID运动控制的路径规划方法。通过在传统A*算法的代价函数中引入障碍物代价项和动... 为应对煤矿巡检机器人在复杂、动态的井下巷道环境中进行路径规划时面临的安全性不足和跟踪精度差的挑战,本文提出了一种融合改进A*全局规划与模糊PID运动控制的路径规划方法。通过在传统A*算法的代价函数中引入障碍物代价项和动态加权策略,提高了全局路径规划的效率和安全性。采用B样条曲线对初始路径进行平滑处理,使路径更符合机器人运动学约束,增强可执行性与轨迹平滑性。设计了基于机器人运动模型的模糊PID控制器,替代传统PID,通过模糊控制自适应整定PID参数,实现了对平滑后全局路径的高精度、高稳定性跟踪控制,有效耦合了线速度与角速度控制。MATLAB和ROS Gazebo仿真实验结果表明,改进A*算法减少了搜索节点约65%,B样条处理显著提升路径平滑度,模糊模型PID控制器相比传统PID在路径跟踪精度和稳定性方面表现更优。最大横向误差范围在±0.05米内,最大航向误差控制在±0.2弧度范围内。该方法显著提升了煤矿巡检机器人路径规划与跟踪性能。 展开更多
关键词 巡检机器人 改进A*算法 B样条路径平滑 模糊PID 路径规划
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基于双层模糊控制与改进遗传算法的移动机器人路径规划算法 认领 引用 被引量：5
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作者 田敏 吴晓枫 《计算机应用研究》 CSCD 北大核心 2026年第1期183-190,共8页
为提升移动机器人在复杂道路环境下执行多任务时的路径安全性与规划效率,提出了一种结合双层模糊控制系统和改进遗传算法的方法,以应对移动机器人路径规划中复杂道路环境与多元化任务安全性的挑战。该方法首先构建了一个基于专家系统的... 为提升移动机器人在复杂道路环境下执行多任务时的路径安全性与规划效率,提出了一种结合双层模糊控制系统和改进遗传算法的方法,以应对移动机器人路径规划中复杂道路环境与多元化任务安全性的挑战。该方法首先构建了一个基于专家系统的双层模糊控制系统:第一层将复杂路况和障碍物转换为道路安全度等级,第二层结合任务安全等级生成遗传算法的适应度权重。通过引入道路安全因素优化遗传算法的适应度函数模型,增强路径规划的安全性。算法实现上,采用伯努利混沌映射、Gaussian算子和Symmetrical Sigmoid算子优化选择、交叉、变异操作,提升了全局搜索能力和效率。实验表明,该方法相较于其他算法,路径距离最大减少5.9%,转弯次数最大减少85.7%,且在多种对比实验中表现出优异的普适性和鲁棒性,有效解决了复杂道路环境与任务安全需求之间的关系。 展开更多
关键词 改进遗传算法 路径规划 双层模糊控制系统 移动机器人
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融合ROUSTIDA和改进的概率直觉模糊聚类的协同过滤推荐算法 认领 引用
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作者 张艳菊 吴一玄 陈泽荣 《计算机工程与科学》 CSCD 北大核心 2026年第4期731-742,共12页
模糊聚类衡量用户评价的模糊性并将相似用户划分为同一簇,能够改善传统协同过滤算法的可扩展性和数据稀疏性,但现有基于模糊聚类的协同过滤算法通常没有考虑聚类中心初始化和模糊集权重的问题,造成聚类效果不稳定和无法全面利用评价信... 模糊聚类衡量用户评价的模糊性并将相似用户划分为同一簇,能够改善传统协同过滤算法的可扩展性和数据稀疏性,但现有基于模糊聚类的协同过滤算法通常没有考虑聚类中心初始化和模糊集权重的问题,造成聚类效果不稳定和无法全面利用评价信息的问题,影响推荐精度。针对上述问题,提出了一种融合ROUSTIDA和改进的概率直觉模糊聚类的协同过滤推荐算法。该算法基于粗糙集理论中的属性约简规则,并以缺失矩阵与相似矩阵的差异最小为原则填补缺失数据,降低数据稀疏性,引入密度函数初始化方法并完成聚类中心的选择,缓解模糊聚类对初始聚类中心的高敏感度,在聚类计算中分别求解隶属度和非隶属度的概率权重和犹豫度相关系数,以添加权重的概率欧氏距离作为聚类的邻近函数以筛选出相关邻居集合,在聚类过程中保留了更多的用户评价信息。在MovieLens 100K和Jester数据集上的实验结果显示,相较于UFCM与FCM-Slope One等其他基于模糊聚类的推荐算法,所提算法的MAE与RMSE指标更低,有更好的推荐精度。 展开更多
关键词 协同过滤 ROUSTIDA算法 密度函数 改进的概率直觉模糊聚类 推荐算法
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