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| 1 | Modified binary particle swarm optimization显示文摘This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype-phenotype rep-resentation and the mutation operator of genetic algorithms.Its main feature is that the BPSO can be treated as a continuous PSO.The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO.Experimental results show that the modified BPSO outperforms the original BPSO algorithm. | Sangwook Lee Sangmoon Soak Sanghoun Oh Witold Pedrycz Moongu Jeon | 2008 | Progress in Natural Science:Materials International2008，18，9： | 11 |
| 2 | Granular Computing for Data Analytics:A Manifesto of Human-Centric Computing显示文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data. | Witold Pedrycz | 2018 | IEEE/CAA Journal of Automatica Sinica2018，5，6： | 9 |
| 3 | 基于粒度聚类的铁矿石烧结过程运行性能评价显示文摘烧结过程的运行性能是生产效率和能源利用的综合表现.运行性能评价是保持烧结过程的运行性能处于最优等级的前提.考虑到时间序列数据的冗余,提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法.首先,利用单因素方差分析方法选取影响运行性能等级的检测参数;然后,采用多粒度区间信息粒化实现检测参数时间序列数据的降维,并进行粒度聚类,得到聚类标签;最后,以聚类得到的聚类标签为输入,利用随机森林算法进行运行性能等级评价.利用实际钢铁企业的运行数据进行实验,构建两个对比实验,分别采用基于时间序列数据聚类(Time series data clustering,TSDC)方法和基于时间序列特征聚类(Time series feature clustering,TSFC)方法.实验结果表明,该方法为有效评价烧结过程的运行性能提供了一套可行方案,为操作人员提升烧结过程运行性能提供了有力的指导. | 杜胜 吴敏 陈略峰 PEDRYCZ Witold | 2023 | 自动化学报2023，49，6： | 3 |
| 4 | Residual-driven Fuzzy C-Means Clustering for Image Segmentation显示文摘In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in clustering.We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise.Built on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise.Besides,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image itself.Supporting experiments on synthetic,medical,and real-world images are conducted.The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers. | Cong Wang Witold Pedrycz ZhiWu Li MengChu Zhou | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，4： | 3 |
| 5 | Fuzzy set technology in knowledge discovery显示文摘 | Witold Pedrycz | 1998 | Fuzzy Sets and Systems1998，98，3： | 2 |
| 6 | A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction显示文摘In recent years,vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction,and many methods have been proposed and successfully applied,such as particle swarm optimization and genetic algorithm.However,the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and challenging.In view of these problems,we propose a population randomization-based multi-objective genetic algorithm.The gesture signal is processed with a slight change by imitating the biological evolution mechanisms.In the proposed algorithm,a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population.The weights of the objective function and the elite retention strategy are adopted,and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation.To verify the effectiveness of the algorithm,preliminary application experiments are performed on the gesture adaptation of a robotic arm.The results are compared with the original signal,and the comparison shows that by using the proposed method,the energy consumption is reduced,and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm.These obtained results meet the communication requirements for human-robot interactions such as handshakes.Moreover,the proposed method has better performance,uses less energy,and has a smaller tracking error than the particle swarm optimization,the single-objective genetic algorithm,and the traditional multi-objective genetic algorithm.A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time. | Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA | 2021 | Science China(Information Sciences)2021，64，1： | 2 |
| 7 | An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem显示文摘The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. | Bingjie Li Guohua Wu Yongming He Mingfeng Fan Witold Pedrycz | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，7： | 2 |
| 8 | A multiobjeetive design of a patient and anaesthetist-friendly neuromuscular blockade controller显示文摘 | FAZENDEIRO P OLIVEIRA J V D PEDRYCZ W | 2007 | IEEE Transactions on Biomedical Engineering2007，54，9： | 1 |
| 9 | The Equivalence Between Fuzzy Mealy and Moore Machines显示文摘 | Li Yongming Pedrycz W | 2006 | Soft Computing2006，10，10： | 1 |
| 10 | Fuzzy Sets in Pattern Recognition:Methodology and Methods显示文摘 | Pedrycz W | 1990 | Pattern Recognition1990，23，12： | 1 |
| 11 | Fuzzy clustering with partial supervision 显示文摘 | Pedrycz Witold Waletzky | 1997 | IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics1997，27，5： | 1 |
| 12 | OR/AND neuron in modeling Fuzzy set connective显示文摘 | HIROTA K PEDRYCZ W | 1994 | IEEE Transactions on Fuzzy Systems1994，2，2： | 1 |
| 13 | A fuzzy extension of Saaty's priority theory显示文摘 | Laarhoven P J M Pedrycz W | | 0，，1： | 1 |
| 14 | A fuzzy extension of Saaty's priority theory显示文摘 | VAN LAARHOVEN P J M PEDRYCZ W | | 0，，02： | 1 |
| 15 | Collaborative Fuzzy Clustering with the use of Fuzzy C-Means and its Quantification显示文摘 | Pedrycz W Rai P | | 0，，18： | 1 |
| 16 | An extended VIKORmethod based on prospect theory for multiple attribute de-cision making under interval type-2 fuzzy environment显示文摘 | QIN J D UU X W PEDRYCZ W | 2015 | Knowledge-Based Systems2015，86，： | 1 |
| 17 | A fuzzy extension of saaty's priority theory显示文摘 | Loargoven V Pedrycz W | 1983 | Fuzzy Sets and Systems1983，11，1： | 1 |
| 18 | Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering显示文摘 | Staiano A Tagliaferri R Pedrycz W | 2006 | Neurocomputing2006，69，： | 1 |
| 19 | Enhancement of fuzzy clustering by mechanisms of partial supervision 显示文摘 | Abdelhamid Bouchachia Witold Pedrycz | 2006 | Fuzzy Setsand Systems2006，157，13： | 1 |
| 20 | Construction of fuzzy models through clustering techniques显示文摘 | Y Yasugawa W Pedrycz K Hirota | 1993 | Fuzzy Sets and Systems1993，54，： | 1 |
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