International Journal of Uncertainty Fuzziness and Knowledge-Based Systems最新文献_第7页

Acknowledgements to the Referees (2019) 对推荐人的感谢(2019)

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2020-02-01 DOI: 10.1142/s0218488520970016

引用次数: 0

Interval Methods in Knowledge Representation 知识表示中的区间方法

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2020-02-01 DOI: 10.1142/s0218488520970028

引用次数: 0

A Multi-Scale Fuzzy Spatial Analysis Framework for Large Data Based on IT2 FS 基于IT2 FS的大数据多尺度模糊空间分析框架

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2015-02-15 DOI: 10.1142/S021848851550004X

Gu Jifa, Mao Jian, Cui Tie-jun, Li Chongwei

{"title":"A Multi-Scale Fuzzy Spatial Analysis Framework for Large Data Based on IT2 FS","authors":"Gu Jifa, Mao Jian, Cui Tie-jun, Li Chongwei","doi":"10.1142/S021848851550004X","DOIUrl":"https://doi.org/10.1142/S021848851550004X","url":null,"abstract":"The geographical world is an intricate system that comprises the interaction of the Earth's atmosphere, hydrosphere, biosphere, lithosphere, and pedosphere. Existing technologies and systems can only store, represent, and analyze crisp or type-I fuzzy spatial data and obtain spatial knowledge on several discrete scales. However, these technologies are limited to multi-scale and high-order vagueness spatial data representation and analysis, particularly regarding the representation and acquisition of multi-scale knowledge. In this paper, the uncertainty in geographic information systems (GISs) and existing problems in classical spatial analysis methods are summarized. Innovative concepts, such as the scale aggregation model and scale polymorphism, are proposed. A multi-scale fuzzy spatial analysis framework based on an interval type-II fuzzy set is introduced, and critical points are highlighted, such as an interval type-II fuzzy geographical object model (the boundary model and metric methods for geometric properties), direction relations, topological relations, and overlap methods. An actual case based on a multi-scale regional debris-flow hazard assessment is used to confirm the validity of the theory proposed in this paper.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"1 1","pages":"73-104"},"PeriodicalIF":1.5,"publicationDate":"2015-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80497335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL 极值学习机与模糊积分的融合

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400138

Junhai Zhai, Hong-Yu Xu, Yan Li

引用次数: 28

FUZZY EXTREME LEARNING MACHINE FOR A CLASS OF FUZZY INFERENCE SYSTEMS 一类模糊推理系统的模糊极值学习机

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400151

Hai-Jun Rong, G. Huang, Yong-Qi Liang

{"title":"FUZZY EXTREME LEARNING MACHINE FOR A CLASS OF FUZZY INFERENCE SYSTEMS","authors":"Hai-Jun Rong, G. Huang, Yong-Qi Liang","doi":"10.1142/S0218488513400151","DOIUrl":"https://doi.org/10.1142/S0218488513400151","url":null,"abstract":"Recently an Online Sequential Fuzzy Extreme Learning (OS-Fuzzy-ELM) algorithm has been developed by Rong et al. for the RBF-like fuzzy neural systems where a fuzzy inference system is equivalent to a RBF network under some conditions. In the paper the learning ability of the batch version of OS-Fuzzy-ELM, called as Fuzzy-ELM is further evaluated to train a class of fuzzy inference systems which can not be represented by the RBF networks. The equivalence between the output of the fuzzy system and that of a generalized Single-Hidden Layer Feedforward Network as presented in Huang et al. is shown first, which is then used to prove the validity of the Fuzzy-ELM algorithm. In Fuzzy-ELM, the parameters of the fuzzy membership functions are randomly assigned and then the corresponding consequent parameters are determined analytically. Besides an input variable selection method based on the correlation measure is proposed to select the relevant inputs as the inputs of the fuzzy system. This can avoid the exponential increase of number of fuzzy rules with the increase of dimension of input variables while maintaining the testing performance and reducing the computation burden. Performance comparison of Fuzzy-ELM with other existing algorithms is presented using some real-world regression benchmark problems. The results show that the proposed Fuzzy-ELM produces similar or better accuracies with a significantly lower training time.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"11 1","pages":"51-61"},"PeriodicalIF":1.5,"publicationDate":"2013-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78609699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

RECOGNIZING TRANSPORTATION MODE ON MOBILE PHONE USING PROBABILITY FUSION OF EXTREME LEARNING MACHINES 基于极限学习机概率融合的手机交通模式识别

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400126

Shuangquan Wang, Yiqiang Chen, Zhenyu Chen

引用次数: 14

DIESEL ENGINE MODELLING USING EXTREME LEARNING MACHINE UNDER SCARCE AND EXPONENTIAL DATA SETS 基于极限学习机的稀缺指数数据集柴油机建模

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400187

P. Wong, C. Vong, C. Cheung, K. Wong

{"title":"DIESEL ENGINE MODELLING USING EXTREME LEARNING MACHINE UNDER SCARCE AND EXPONENTIAL DATA SETS","authors":"P. Wong, C. Vong, C. Cheung, K. Wong","doi":"10.1142/S0218488513400187","DOIUrl":"https://doi.org/10.1142/S0218488513400187","url":null,"abstract":"To predict the performance of a diesel engine, current practice relies on the use of black-box identification where numerous experiments must be carried out in order to obtain numerical values for model training. Although many diesel engine models based on artificial neural networks (ANNs) have already been developed, they have many drawbacks such as local minima, user burden on selection of optimal network structure, large training data size and poor generalization performance, making themselves difficult to be put into practice. This paper proposes to use extreme learning machine (ELM), which can overcome most of the aforementioned drawbacks, to model the emission characteristics and the brake-specific fuel consumption of the diesel engine under scarce and exponential sample data sets. The resulting ELM model is compared with those developed using popular ANNs such as radial basis function neural network (RBFNN) and advanced techniques such as support vector machine (SVM) and its variants, namely least squares support vector machine (LS-SVM) and relevance vector machine (RVM). Furthermore, some emission outputs of diesel engines suffer from the problem of exponentiality (i.e., the output y grows up exponentially along input x) that will deteriorate the prediction accuracy. A logarithmic transformation is therefore applied to preprocess and post-process the sample data sets in order to improve the prediction accuracy of the model. Evaluation results show that ELM with the logarithmic transformation is better than SVM, LS-SVM, RVM and RBFNN with/without the logarithmic transformation, regardless the model accuracy and training time.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"1 1","pages":"87-98"},"PeriodicalIF":1.5,"publicationDate":"2013-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79285323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

EVOLVING EXTREME LEARNING MACHINE PARADIGM WITH ADAPTIVE OPERATOR SELECTION AND PARAMETER CONTROL 演化的具有自适应算子选择和参数控制的极限学习机范式

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400229

Ke Li, Ran Wang, S. Kwong, Jingjing Cao

引用次数: 33

STATE-ACTION VALUE FUNCTION MODELED BY ELM IN REINFORCEMENT LEARNING FOR HOSE CONTROL PROBLEMS 胶管控制问题强化学习中elm模型的状态-行为值函数

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-10-31 DOI: 10.1142/S0218488513400199

J. M. López-Guede, B. Fernández-Gauna, M. Graña

引用次数: 15

An analysis of ELM approximate error based on random weight matrix 基于随机权矩阵的ELM近似误差分析