Signatures: Definitions, operators and applications to fuzzy modelling

Pozna, Claudiu; Minculete, Nicuşor; Precup, Radu‐Emil; Kóczy, László T.; Ballagi, Áron

doi:10.1016/j.fss.2011.12.016

Cited by 85 publications

(31 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many algorithms can be used as efficient decision functions, especially when the fuzzy soft set ( F, A) has many objects, such as fuzzy S-trees, signature trees, t-concept lattice, Artificial Bee Colony (ABC) algorithm [28][29][30][31], and so on. Here, we use the widely used Hamming distance.…”

Section: The Decision Function-hamming Distancementioning

confidence: 99%

“…Secondly, in the traditional ideal solution algorithm, it can be seen that the normalization process is the process of establishing the membership function µ(x). There are many commonly used normalization methods, i.e., Equation (29), but few of them can reflect the nature of the problem. The fuzzy soft set already contains the membership function µ(x), which can be used well.…”

Section: The Decision-making Based On Fuzzy Soft Sets and Ideal Solutionmentioning

confidence: 99%

See 1 more Smart Citation

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

2017

View full text Add to dashboard Cite

Abstract:In this paper, a decision model based on a fuzzy soft set and ideal solution approaches is proposed. This new decision-making method uses the divide-and-conquer algorithm, and it is different from the existing algorithm (the choice value based approach and the comparison score based approach). The ideal solution is generated according to each attribute (pros or cons of the attributes, with or without constraints) of the fuzzy soft sets. Finally, the weighted Hamming distance is used to compute all possible alternatives and get the final result. The core of the decision process is the design phase, the existing decision models based on soft sets mostly neglect the analysis of attributes and decision objectives. This algorithm emphasizes the correct expression of the purpose of the decision maker and the analysis of attributes, as well as the explicit decision function. Additionally, this paper shows the fact that the rank reversal phenomenon occurs in the comparison score algorithm, and an example is provided to illustrate the rank reversal phenomenon. Experiments indicate that the decision model proposed in this paper is efficient and will be useful for practical problems. In addition, as a general model, it can be extended to a wider range of fields, such as classifications, optimization problems, etc.

show abstract

Section: The Decision Function-hamming Distancementioning

confidence: 99%

Section: The Decision-making Based On Fuzzy Soft Sets and Ideal Solutionmentioning

confidence: 99%

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

2017

View full text Add to dashboard Cite

show abstract

“…Relevant process and control applications are presented in [35][36][37][38][39][40][41][42][43], with both crisp and fuzzy models. However, the online identification algorithms must be adapted accordingly in order to cope with the specific nonlinear elements and operating conditions of these processes [44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Antilock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously

show abstract

“…Various fuzzy modeling approaches are proposed in literature and applied in different fields for data analysis and knowledge exploring (for examples, see [24][25][26][27]). …”

Section: Introductionmentioning

confidence: 99%

Time Series Seasonal Analysis Based on Fuzzy Transforms

Martino

Sessa

2017

Symmetry

View full text Add to dashboard Cite

Abstract:We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 to 2015 making predictions on mean temperature, max temperature and min temperature, all considered daily. In the second example, we use the daily mean temperature measured at the weather station "Chiavari Caperana" in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, Auto Regressive Integrated Moving Average (ARIMA) and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples. In addition, the comparison results show that, for seasonal time series that have no consistent irregular variations, the performance obtained with our method is comparable with the ones obtained using Support Vector Machine-and Artificial Neural Networks-based models.

show abstract

Signatures: Definitions, operators and applications to fuzzy modelling

Cited by 85 publications

References 43 publications

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Time Series Seasonal Analysis Based on Fuzzy Transforms

Contact Info

Product

Resources

About