Interval type-2 fuzzy c-means clustering using intuitionistic fuzzy sets

Nguyen, Dzung Dinh; Ngo, Long Thanh

doi:10.1109/wict.2013.7113152

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…Definition 2 [35]: For type-2 intuitionistic fuzzy sets, the characteristic is the boundary function of membership degree and the boundary function of non-membership degree, which are defined asμÃ * (x), µÃ * (x),νÃ * (x) and νÃ * (x). All elements meet the constraints:…”

Section: B Type-2 Intuitionistic Fuzzy Sets (T2ifs)mentioning

confidence: 99%

“…And, in 1989, Atanassov and Gargov [31] proposed the concept of interval-valued intuitionistic fuzzy sets, where they pointed out that interval-valued ones were equivalent to intuitionistic fuzzy sets. By means of the modeling ability to realistic uncertainty, IFSs have attracted wide attentions and been applied in various fields, such as medical diagnosis [32], pattern recognitions [33], decisionmaking [34] and image processing [35]. As an extension of IFSs, Eyoh et al [36] combined the concept of intuitionistic fuzzy sets with the interval type-2 fuzzy logic, and proposed a novel prediction method.…”

Section: Introductionmentioning

confidence: 99%

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Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Wang

Luo

2019

IEEE Access

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Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme. INDEX TERMS Fuzzy neural network, intuitionistic fuzzy sets, interval type-2 fuzzy systems, metacognition, sequential learning.

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Section: B Type-2 Intuitionistic Fuzzy Sets (T2ifs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Wang

Luo

2019

IEEE Access

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“…Again some research has been done in this area. (Nguyen , Ngo & Pham, 2013) proposed Intuitionistic Interval Type-2 Fuzzy C-means Clustering (InIT2FCM) to handle clustering related problems. They introduced Intuitionistic Fuzzy Sets (IFS) and Intuitionistic Type-2 Fuzzy Sets (InIT2FS) for handling data uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop

Yeasmin¹,

Hussain²,

Sikder³

et al. 2018

CIS

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Over the decades there is a high demand of a tool to identify the nutritional needs of the people of Bangladesh since it has an alarming rate of under nutrition among the countries of the world. This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.

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“…π c,ik (t + 1) = π c,ik (t) − γ δE ∂π c,ik (11) π var,ik (t + 1) = π var,ik (t) − γ δE ∂π var,ik (12) where γ is the learning rate(step size) that must be carefully chosen as a large value may lead to instability, and small value on the other hand may lead to a slow learning process.…”

Section: Inferencementioning

confidence: 99%

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Eyoh

John

Maere

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parameters of the IT2IFLS. The empirical comparison is made on the designed system using two synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in terms of non-membership functions and hesitation indexes in IT2IFLS tend to reduce the root mean square error (RMSE) of the system compared to a type-1 fuzzy logic approach and some interval type-2 fuzzy systems.

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Interval type-2 fuzzy c-means clustering using intuitionistic fuzzy sets

Cited by 22 publications

References 14 publications

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

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