2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献

Evaluation of causal sentences in automated summaries 自动摘要中因果句的评价

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-09-28 DOI: 10.1109/FUZZ-IEEE.2017.8015666

C. Puente, Augusto Villa Monte, L. Lanzarini, Alejandro Sobrino, J. A. Olivas

引用次数: 4

A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors 基于可穿戴传感器的自适应神经模糊与贝叶斯相结合的步态事件识别与预测策略

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015447

Uriel Martinez-Hernandez, Adrian Rubio Solis, G. Panoutsos, A. Dehghani

{"title":"A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors","authors":"Uriel Martinez-Hernandez, Adrian Rubio Solis, G. Panoutsos, A. Dehghani","doi":"10.1109/FUZZ-IEEE.2017.8015447","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015447","url":null,"abstract":"A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121961274","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: 8

Decision making problem solving using fuzzy networks with rule base aggregation 基于规则库聚合的模糊网络决策问题求解

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015423

A. M. Yaakob, A. Gegov, Siti Fatimah Abdul Rahman

{"title":"Decision making problem solving using fuzzy networks with rule base aggregation","authors":"A. M. Yaakob, A. Gegov, Siti Fatimah Abdul Rahman","doi":"10.1109/FUZZ-IEEE.2017.8015423","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015423","url":null,"abstract":"This paper presents a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method. The method is based on aggregation of rules with different linguistic values of the output of fuzzy networks to solve multi criteria decision-making problems whereby both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for decision process and further observes the performance for both benefit and cost criteria. The aggregation of rule bases in a fuzzy system maps the fuzzy membership functions for all rules to an aggregated fuzzy membership function representing the overall output for the rules. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. To ensure practicality and effectiveness, the proposed method is further tested on equity selection problems. The ranking produced by the method is comparatively validated using Spearman rho rank correlation. The results show that the proposed method outperforms the existing TOPSIS approaches in terms of ranking.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209490","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

Interpretability indices for hierarchical fuzzy systems 层次模糊系统的可解释性指标

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015616

T. R. Razak, J. Garibaldi, Christian Wagner, A. Pourabdollah, D. Soria

引用次数: 27

Fuzzycreator: A python-based toolkit for automatically generating and analysing data-driven fuzzy sets Fuzzycreator:一个基于python的工具集，用于自动生成和分析数据驱动的模糊集

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015445

J. McCulloch

引用次数: 17

Strategic weight manipulation in multiple attribute decision making in an incomplete information context 不完全信息环境下多属性决策中的策略权重操纵

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015419

Yating Liu, Yucheng Dong, F. Chiclana, F. J. Cabrerizo, E. Herrera-Viedma

引用次数: 10

Fuzzy approach for sentiment analysis 情感分析的模糊方法

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015577

Chris Jefferson, Han Liu, Ella Haig

{"title":"Fuzzy approach for sentiment analysis","authors":"Chris Jefferson, Han Liu, Ella Haig","doi":"10.1109/FUZZ-IEEE.2017.8015577","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015577","url":null,"abstract":"Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132676228","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: 53

Landing on the moon with type-2 fuzzy logic 用二类模糊逻辑登陆月球

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015500

Atakan Sahin, T. Kumbasar

{"title":"Landing on the moon with type-2 fuzzy logic","authors":"Atakan Sahin, T. Kumbasar","doi":"10.1109/FUZZ-IEEE.2017.8015500","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015500","url":null,"abstract":"In this study, we will present the novel application of Type-2 (T2) fuzzy logic to the popular video game called Lunar Lander. The proposed T2 fuzzy moon landing system structure is composed of the error signal generator and the T2 fuzzy logic control structure which give the opportunity to transform the moon landing problem of the spaceship as a multivariable tracking control problem. The landing problem of the game can be seen as one of the classical multivariable control problems including uncertainties due to the randomization process occurring the game environment. Thus, we will employ T2 fuzzy logic controllers since they are capable of handling a high level of uncertainties. Then, by optimizing the T2 fuzzy moon landing system via the particle swarm optimization, we will show that the resulting T2 fuzzy moon landing system resulted with an adequate control and game performance in the presence of the uncertainties, disturbances and nonlinear system dynamics in comparison with its type-1 and conventional counterparts. We believe that the results of this paper will be an important step for a wider deployment of T2 fuzzy logic in the research area of computer games.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773392","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

Z-TOPSIS approach for performance assessment using fuzzy similarity 采用Z-TOPSIS方法进行模糊相似度绩效评价

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015458

K. Khalif, A. Gegov, Ahmad Syafadhli Abu Bakar

{"title":"Z-TOPSIS approach for performance assessment using fuzzy similarity","authors":"K. Khalif, A. Gegov, Ahmad Syafadhli Abu Bakar","doi":"10.1109/FUZZ-IEEE.2017.8015458","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015458","url":null,"abstract":"This paper presents fuzzy similarity based Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) for z-numbers. The classical fuzzy TOPSIS techniques use closeness coefficient to determine the rank order by calculating Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) simultaneously. The authors propose fuzzy similarity to replace closeness coefficient by doing ranking evaluation. Fuzzy similarity is used to calculate the similarity between two fuzzy ratings (FPIS and FNIS). Fuzziness is not sufficient enough when dealing with real information and a degree of reliability of the information is very critical. Hence, the implementation of z-numbers is taken into consideration as they can capture better the knowledge of human being and are extensively used in uncertain information development to deal with linguistic decision making problems. A numerical example is given to illustrate the application of the proposed technique in ranking company performance assessment. The results show that it is highly feasible to use the proposed technique in performance assessment.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134173085","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

The arithmetic recursive average as an instance of the recursive weighted power mean 将算术递归平均作为递归加权平均的一个实例

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015507

Christian Wagner, T. Havens, Derek T. Anderson

{"title":"The arithmetic recursive average as an instance of the recursive weighted power mean","authors":"Christian Wagner, T. Havens, Derek T. Anderson","doi":"10.1109/FUZZ-IEEE.2017.8015507","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015507","url":null,"abstract":"The aggregation of multiple information sources has a long history and ranges from sensor fusion to the aggregation of individual algorithm outputs and human knowledge. A popular approach to achieve such aggregation is the fuzzy integral (FI) which is defined with respect to a fuzzy measure (FM) (i.e. a normal, monotone capacity). In practice, the discrete FI aggregates information contributed by a discrete number of sources through a weighted aggregation (post-sorting), where the weights are captured by a FM that models the typically subjective ‘worth’ of subsets of the overall set of sources. While the combination of FI and FM has been very successful, challenges remain both in regards to the behavior of the resulting aggregation operators — which for example do not produce symmetrically mirrored outputs for symmetrically mirrored inputs — and also in a manifest difference between the intuitive interpretation of a stand-alone FM and its actual role and impact when used as part of information fusion with a FI. This paper elucidates these challenges and introduces a novel family of recursive average (RAV) operators as an alternative to the FI in aggregation with respect to a FM; focusing specifically on the arithmetic recursive average. The RAV is designed to address the above challenges, while also facilitating fine-grained analysis of the resulting aggregation of different combinations of sources. We provide the mathematical foundations of the RAV and include initial experiments and comparisons to the FI for both numeric and interval-valued data.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115914205","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: 7