Intuitionistic time series fuzzy inference system

Eğrioğlu, Erol; Baş, Eren; Yolcu, Özge Cağcağ; Yolcu, Ufuk

doi:10.1016/j.engappai.2019.03.024

Cited by 20 publications

(13 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy inference system (FIS) is based on fuzzy set theory and IF-THEN fuzzy rules. The basic structure of fuzzy is built based on the three components namely, (1) rule base (containing selected fuzzy rules), (2) database or data dictionary (defining membership functions to be used in the rule base) and (3) reasoning mechanism (performing inference procedures on a given rule base to get results which can be used) [10] [15]. Defuzzification input is a fuzzy set of results from aggregation and output in the form of a single number to be entered into the FIS output variable.…”

Section: Research Methods 21 Fuzzy Inference Systemmentioning

confidence: 99%

See 1 more Smart Citation

Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

Noviandi

Ilham

2020

KINETIK

View full text Add to dashboard Cite

Rainfall which is occurred in an area explain the Onset Rainy Season (ORS). ORS is a characteristic of the rainy season which is important to know, but the characteristics of the rain itself is very difficult to predict. We use the method of Fuzzy Inference System (FIS) to predict ORS. Unfortunately, FIS is weak to determine parameters so that influences the working FIS method. In this study, we use PSO to optimize parameter of the FIS method to increase perform of the FIS method for onset prediction of the rainy season with the predictor Sea Surface Temperature Nino 3.4 and Index Ocean Dipole. We used coefficient correlation to determine the relationship between two variables as predictors and RMSE as evaluate to all methods. The experiment result has shown that the work of FIS-PSO after optimizing produced the good work with the coefficient correlation = 0.57 and RMSE = 2.96 that is the smallest value that is better performance if compared with other methods. It can be concluded that the method proposed can increase the onset prediction of the rainy season.

show abstract

Section: Research Methods 21 Fuzzy Inference Systemmentioning

confidence: 99%

“…In this study we will use Mamdani method. The Fuzzy Mamdani method is easier to use and accurate in producing a decision in the form of a fuzzy set that is easy to understand because its best suits human instincts [8] [10].…”

mentioning

confidence: 99%

Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

Noviandi

Ilham

2020

KINETIK

View full text Add to dashboard Cite

show abstract

“…After the work by Castillo et al [15], an increasing number of studies in recent years have extended FTS to IFTS [16][17][18][19][20]. However, most of these studies focused on improving prediction results and did not consider uncertainty or hesitation when facing forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these studies focused on improving prediction results and did not consider uncertainty or hesitation when facing forecasting problems. For example, Egrioglu et al [19] integrated particle swarm optimization into the process of IFTS to improve the weights of rules. In 2018, Danish Lohani et al [18] adopted the intuitionistic fuzzy C-means to obtain reasonable IFS.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

Chen¹,

Chen²,

Chang³

et al. 2021

IJCIS

View full text Add to dashboard Cite

Traditional time series methods can predict seasonal problems, but not problems with transferred linguistic data. Thus, a forecasting method for such problems is required. However, existing intuitionistic fuzzy time series forecasting methods lack persuasiveness in determining the degree of hesitation and the lengths of intervals. Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series. This paper proposes the weighted intuitionistic fuzzy time series model based on the Nth quantile discretization approach (NQDA). The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data. In the experimental section, the forecasts of several data sets are made for model validation. Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.

show abstract

“…Various extensions of FIS on advanced sets have been made properly to deal with the changes in the studied problems. Intuitionistic fuzzy inference system (IFIS) was used in regression and bankruptcy prediction in which the non-membership degree dominates [23][24][25][26][27][28]. FIS was also extended on the Picture Fuzzy Set (PFS) with the combination with picture fuzzy clustering and picture fuzzy measures [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

et al. 2020

View full text Add to dashboard Cite

Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

show abstract

Intuitionistic time series fuzzy inference system

Cited by 20 publications

References 91 publications

Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

Contact Info

Product

Resources

About