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International Conference on Fuzzy Systems最新文献

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The Unbalanced Linguistic Ordered Weighted Averaging operator 非平衡语言有序加权平均算子
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584199
David Isern, Lucas Marin, A. Valls, A. Moreno
{"title":"The Unbalanced Linguistic Ordered Weighted Averaging operator","authors":"David Isern, Lucas Marin, A. Valls, A. Moreno","doi":"10.1109/FUZZY.2010.5584199","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584199","url":null,"abstract":"Aggregation operators for linguistic variables usually assume a uniform and symmetrical distribution of the linguistic terms that define the variable. A well-known aggregation operator is the Linguistic Ordered Weighted Average (LOWA), which has been extensively applied. However, there are some problems where an unbalanced set of linguistic terms is more appropriate to describe the objects. In this paper we define the Unbalanced Linguistic Ordered Weighted Average (ULOWA) on the basis of the LOWA operator. ULOWA takes into account the fuzzy membership functions of the terms during the aggregation process. There is no restriction on the form of the membership functions of the terms, which can be triangular or trapezoidal, non symmetrical and non equally distributed. The paper demonstrates the properties of ULOWA. Finally, a comparison of this operator with some other aggregation operators for unbalanced sets of terms is done.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115054325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Acquisition of robot control rules by evolving MDDs 基于mdd进化的机器人控制规则获取
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584043
Masashi Sakai, Yutaro Tomoto, M. Kanoh, Tsuyoshi Nakamura, H. Itoh
{"title":"Acquisition of robot control rules by evolving MDDs","authors":"Masashi Sakai, Yutaro Tomoto, M. Kanoh, Tsuyoshi Nakamura, H. Itoh","doi":"10.1109/FUZZY.2010.5584043","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584043","url":null,"abstract":"A method in which multi-valued decision diagrams (MDDs) are used to acquire robot action rules is proposed. Kanoh et al. have proposed a method, which uses multi-terminal binary decision diagrams (MTBDDs), to acquire robot action rules. But the variables of MTBDDs can only take values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of variables and the MTBDDs that represent the action rules become complex. Here, a method that uses MDDs, in which a single variable can take on multiple values, is proposed and experimental results are shown comparing MTBDDs and MDDs through simulations to acquire robot action rules.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115066224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Providing PRTools with fuzzy rule-based classifiers 为PRTools提供基于模糊规则的分类器
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584343
M. Cococcioni, Eleonora D'Andrea, B. Lazzerini
{"title":"Providing PRTools with fuzzy rule-based classifiers","authors":"M. Cococcioni, Eleonora D'Andrea, B. Lazzerini","doi":"10.1109/FUZZY.2010.5584343","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584343","url":null,"abstract":"This paper first reviews the state-of-the-art of fuzzy rule-based classifiers (FRBCs), then it discusses how to implement an FRBC under the Pattern Recognition Toolbox (PRTools), the de-facto standard toolbox for classification in Matlab. Such an implementation, called frbc, allows for a straightforward comparison of frbc with other classifiers already available under the PRTools. Furthermore, frbc can easily be used and combined with any other general-purpose function already available in PRTools. In this way, e.g., it becomes really easy to perform many types of feature selection, based on the accuracy achieved by frbc on the subset of features at hand. Another useful feature is the capability to export each FRBC generated by frbc as a standard Fuzzy Inference System (FIS) structure used within the Matlab Fuzzy Logic Toolbox (FLT): this allows comparisons/validations, visual inspection of the rule base, etc. In the experimental part we first assess the correctness of the implementation, by reproducing results existing in the literature. Then we show some examples of usage of frbc, combined with existing PRTools functions.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Hybrid Fuzzy-MutiAgent planning for robust mobile robot motion 鲁棒移动机器人运动的模糊-多agent混合规划
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584872
S. Kéfi, H. M. Kammoun, I. Kallel, A. Alimi
{"title":"Hybrid Fuzzy-MutiAgent planning for robust mobile robot motion","authors":"S. Kéfi, H. M. Kammoun, I. Kallel, A. Alimi","doi":"10.1109/FUZZY.2010.5584872","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584872","url":null,"abstract":"This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot; these zone agents compete for attracting the sub-goal. A planning agent, fortified with a fuzzy rule based system, decides on the best sub-goal to reach. Fuzzy-MARCoPlan is simulated and tested on several navigation environments which are generated randomly under the multiagent platform MadKit. These tests confirm the robustness of the proposed system in terms of path optimality in a dynamic environment. Moreover, the obtained results reinforce the advantage of a multiagent planning hybridized with fuzzy reasoning for mobile robot motion planning.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123527822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Forecasting time-series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with on-line inputs selection 基于在线输入选择的进化Takagi-Sugeno (eTS)模糊系统预测神经网络GC1时间序列
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584130
J. Andreu, P. Angelov
{"title":"Forecasting time-series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with on-line inputs selection","authors":"J. Andreu, P. Angelov","doi":"10.1109/FUZZY.2010.5584130","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584130","url":null,"abstract":"In this paper we present results and algorithm used to predict 14 days horizon from a number of time series provided by the NN GC1 concerning transportation datasets [1]. Our approach is based on applying the well known Evolving Takagi-Sugeno (eTS) Fuzzy Systems [2–6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their structure from the data stream on-line and in real-time mode. That means we used all the data samples from the time series only once, at any instant in time we only used one single input vector (which consist of few data samples as described below) and we do not iterate or memorize the whole sequence. It should be emphasized that this is a huge practical advantage which, unfortunately cannot be compared directly to the other competitors in NN GC1 if only precision/error is taken as a criteria. It is also worth to require time for calculations and memory usage as well as iterations and computational complexity to be provided and compared to build a fuller picture of the advantages the proposed technique offers. Nevertheless, we offer a computationally light and easy to use approach which in addition does not require any user-or problem-specific thresholds or parameters to be specified. Additionally, this approach is flexible in terms not only of its structure (fuzzy rule based and automatic self-development), but also in terms of automatic input selection as will be described below.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121956630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Context-aware collective decision making based on fuzzy outranking 基于模糊超越排序的情景感知集体决策
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584875
S. Chandana, H. Leung
{"title":"Context-aware collective decision making based on fuzzy outranking","authors":"S. Chandana, H. Leung","doi":"10.1109/FUZZY.2010.5584875","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584875","url":null,"abstract":"In sensor networks, depending on the user-defined goal and the number of objects-of-interest within the common sensor coverage area, multiple sensors generate multiple sources of information. Combining this information is essential and in this paper we propose a fuzzy outranking approach to combining information at the decision level, therefore leading to a collaborative decision making framework. Decision level information is represented through graphical models which helps in enhancing quantifiable system performance by processing information at a higher level and the second advantage is the ability to implement an adaptive framework for decision making. When used with dynamic belief update and an integrated database, a fuzzy outranking approach can be implemented with the ability to adapt to new sensor information and combined various local sensor decisions.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122017675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fuzzy filtering systems for performing environment improvement of computational DNA motif discovery 用于计算DNA基序发现环境改进的模糊滤波系统
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584550
Dianhui Wang, Sarwar Tapan
{"title":"Fuzzy filtering systems for performing environment improvement of computational DNA motif discovery","authors":"Dianhui Wang, Sarwar Tapan","doi":"10.1109/FUZZY.2010.5584550","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584550","url":null,"abstract":"DNA datasets demonstrate considerably low signal-to-noise ratio that constrains the computational motif discovery tools to achieve satisfactory performances. Thus, reducing the search space and increasing the signal-to-noise ratio (by the means of filtering) can be useful to facilitate computational motif discovery tools with better performing environments. This paper proposes unsupervised fuzzy filtering systems, that aim to remove a large portion of k-mers that are less relevant to potential motif instances in terms of location overlaps in given sequences. Relative Model Mismatch Score (RMMS), which is a new quantitative metric for measuring the quality of motif models, is employed in this work to facilitate the proposed filtering. A modified version of fuzzy c-means clustering algorithm with an initialization strategy is then adopted to group k-mers, while a complement of fuzzified RMMS is used to rank k-mers for data filtering. Experimental results on eight real DNA datasets showed that, the proposed filtering systems could remove approximately (85 ± 5)% of data samples while maintaining a high retention rate of relevant k-mers. Thus, this filtering as a data pre-processing component, will improve the performing environments of the motif discovery tools, since the filtered datasets will contain much smaller cardinality and higher signal-to-noise ratio than the original datasets.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124464119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Enhanced weakly trained frontal face detector for surveillance purposes 增强弱训练正面人脸检测器的监视目的
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584450
W. Louis, K. Plataniotis, Yong Man Ro
{"title":"Enhanced weakly trained frontal face detector for surveillance purposes","authors":"W. Louis, K. Plataniotis, Yong Man Ro","doi":"10.1109/FUZZY.2010.5584450","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584450","url":null,"abstract":"Face detection is becoming popular in surveillance applications; however, the need of enormous size face/non-face dataset, large number of features, and long training time are persistent problems. This paper claims that only a subset of the total number of features conserves the major power to detect faces; hence, this subset is capable to detect faces with high detection rate. The proposed detector fuses the results of two classifiers where one is trained with only 40 Haar-like features and the other is trained with only 50 LBP Histogram features. A pre-processing stage of skin-tone detection is applied to reduce the false positive rate. The detector is examined on real-life low-resolution surveillance sequence. Conducted experiments show that the proposed detector can achieve a high detection rate and a low false positive rate. Also, it outperforms Lienhart detector and tolerates wide range of illumination and blurring changes.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128625501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Type III fuzzy impulsive systems and stability analysis III型模糊脉冲系统及其稳定性分析
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584374
I. Zamani, M. Shafiee
{"title":"Type III fuzzy impulsive systems and stability analysis","authors":"I. Zamani, M. Shafiee","doi":"10.1109/FUZZY.2010.5584374","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584374","url":null,"abstract":"In this paper, a new approach for the stability analysis of continuous-time type III (Takagi-Sugeno-Kang) fuzzy impulsive system is proposed. Based on Lyapunov criterion, some conditions are derived to check stability and exponentially stability of type III fuzzy impulsive systems. These conditions are given in terms of matrix inequalities.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129743461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Fuzzy logic helps to integrate music theory and practice 模糊逻辑有助于音乐理论与实践的结合
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584652
T. León, V. Liern
{"title":"Fuzzy logic helps to integrate music theory and practice","authors":"T. León, V. Liern","doi":"10.1109/FUZZY.2010.5584652","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584652","url":null,"abstract":"Among the many practices of composers, instrumentalists and singers which clearly correspond with fuzzy logic our focus here is on tuning. Different criteria have been used to select the sounds that music uses. A set containing these sounds (musical notes) is called a tuning system. Several tuning systems coexist in a classical orchestra. The pitches of the notes are different and very precisely defined for each system; however the consequences of small deviations from these theoretical frequencies are not serious. Actually, the orchestra members are aware of the necessity of reaching a consensus and adjust their instruments to tune well. Because of this, many musicians feel that the mathematical arguments that justify tuning systems are impractical. Modeling the notes as fuzzy sets provides a flexible theoretical framework which helps to integrate tuning theory and practice. We illustrate our approach comparing some tuning systems and analyzing a excerpt by Béla Bartók.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129813107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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