A Review of Some Methods for Ranking Fuzzy Subsets

Bortolan, G.; Degani, R.

doi:10.1016/b978-1-4832-1450-4.50016-x

Cited by 54 publications

(55 citation statements)

References 17 publications

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“…To obtain crisp value of credit score, we use a defuzzification procedure to obtain the crisp value for decisionmaking purpose. According to Bortolan and Degani [11], the defuzzified value of a triangular fuzzy number ) , , ( In this way, a final group forecasting consensus is computed with the above processes. For illustration and verification purposes, an illustrated numerical example and three typical foreign exchange rates are conducted.…”

Section: Neural-network-based Fuzzy Group Forecasting Methodologymentioning

confidence: 99%

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Lai

Wang

2007

Computational Science – ICCS 2007

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Abstract. This study proposes a novel neural-network-based fuzzy group forecasting model for foreign exchange rates prediction. In the proposed model, some single neural network models are first used as predictors for foreign exchange rates prediction. Then these single prediction results produced by each single neural network models are fuzzified into some fuzzy prediction representations. Subsequently, these fuzzified prediction representations are aggregated into a fuzzy group consensus, i.e., aggregated fuzzy prediction representation. Finally, the aggregated prediction representation is defuzzified into a crisp value as the final prediction results. For illustration and testing purposes, a typical numerical example and three typical foreign exchange rates prediction experiments are presented. Experimental results reveal that the proposed model can significantly improve the prediction performance for foreign exchange rates.

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Section: Neural-network-based Fuzzy Group Forecasting Methodologymentioning

confidence: 99%

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Lai

Wang

2007

Computational Science – ICCS 2007

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“…In this subsection, in order to show how the proposed method works and how viewpoints affect the ranking result, two examples are presented according to [1]. Table 4 is the result of comparison with previous works, in which, the methods refer to [15], [16], [17], [4], [19], [12], [18], [10] and [6], respectively, the content of Table 4 partly comes from [6].…”

Section: Numeric Examples and Comparison With Previous Workmentioning

confidence: 99%

“…Because of the suitability for representing uncertain values, fuzzy numbers have been widely used in many applications. Since fuzzy numbers represent uncertain numeric values, it is difficult to rank them according to their magnitude, Many methods for ranking fuzzy numbers have been proposed [1]- [5], such as, representing them with real numbers [6]- [12]. The results of studies on ranking fuzzy numbers have been used in application areas such as decision making [13]- [22].…”

Section: Introductionmentioning

confidence: 99%

A Method for Ranking Fuzzy Numbers and Its Application to Game with Fuzzy Profit

Shao¹,

Pei²

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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In this paper, a new method for ranking fuzzy numbers is proposed. Based on decomposition theorem of fuzzy set, firstly, the method considers interval numbers, the relation between lines and interval numbers is discussed in Cartesian coordinates, the distance of dot A to lines l is used as evaluation of interval number. Then, integral of all distances is used as evaluation of fuzzy number. Finally, ranking fuzzy numbers is implemented by the evaluations of fuzzy numbers. In order to illustrate the ranking method, numeric example is shown, and for the comparative study, our method is compared with four existing ranking methods. As an example of potential applications, the proposed method is applied to game with Fuzzy Profit.

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“…The algorithm finally returns the best solution found so far which in our case means that one with the smallest E[C max ]. In case we have two solutions A and B with the same E[C max ] we use a ranking from [1] which chooses the solution with the minor modal value and, if the tie persists, then chooses the solution with the minimum support width.…”

Section: Tabu Search For Fjspmentioning

confidence: 99%

Hybrid Tabu Search for Fuzzy Job Shop

Palacios

Puente

González-Rodríguez

et al. 2013

Natural and Artificial Models in Computation and Biology

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Abstract. We consider the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, with uncertainty in task durations that we model using fuzzy numbers. We propose a tabu search algorithm for minimising the expected makespan based on reversing arcs within critical blocks. We test the algorithm and then combine it with a genetic algorithm from the literature so we can observe the synergy effect, obtaining better results with the hybrid algorithm than with its components by separate. Finally we compare our hybrid algorithm with a memetic algorithm from the literature and show that even in similar times, our method is better in terms of expected makespan.

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A Review of Some Methods for Ranking Fuzzy Subsets

Cited by 54 publications

References 17 publications

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

A Method for Ranking Fuzzy Numbers and Its Application to Game with Fuzzy Profit

Hybrid Tabu Search for Fuzzy Job Shop

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