Numerical methods for solving fuzzy equations: A survey

Jafari, Raheleh; Yu, Wen; Razvarz, Sina; Gegov, Alexander

doi:10.1016/j.fss.2019.11.003

Cited by 25 publications

(8 citation statements)

References 57 publications

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“…In these cases, we can consider the fuzzy preferences and express them as fuzzy numbers. We can represent these quantities as fuzzy triangular numbers whose membership function is one of the best known to represent fuzzy components in fuzzy optimisation problems [93]. Thus, the fuzzy number [c] α = {a ∈ R : c(a) ≥ α} where 1 ≥ a > 0, c ∈ E is a triangular number.…”

Section: Fuzzy Optimizationmentioning

confidence: 99%

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

2021

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Tourism has direct and indirect implications for CO2 emissions. Therefore, it is necessary to develop tourism management based on sustainable tourism, mainly in the transport process. Tourist itinerary planning is a complex process that plays a crucial role in tourist management. This type of problem, called the tourist trip design problem, aims to build personalised itineraries. However, planning tends to be biased towards group travel with heterogeneous preferences. Additionally, much of the information needed for planning is vague and imprecise. In this paper, a new model for tourist route planning is developed to minimise CO2 emissions from transportation and generate an equitable profit for tourists. In addition, the model also plans group routes with heterogeneous preferences, selects transport modes, and addresses uncertainty from fuzzy optimisation. A set of numerical tests was carried out with theoretical and real-world instances. The experimentation develops different scenarios to compare the results obtained by the model and analyse the relationship between the objectives. The results demonstrate the influence of the objectives on the solutions, the direct and inverse relationships between objectives, and the fuzzy nature of the problem.

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Section: Fuzzy Optimizationmentioning

confidence: 99%

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

2021

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“…Artificial intelligence techniques are viewed as the most efficient techniques in recent decades [7][8][9][10][11][12][13][14][15][16][17][18]. For example, Fuzzy logic systems are broadly utilized to model the systems characterizing vague and unreliable information [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. In artificial intelligence area [39,40], end-to-end dialog systems have been attained interest because of the current progress of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Memory Networks: A Survey

Jafari

Razvarz

Gegov

2020

Advances in Intelligent Systems and Computing

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Constructing a dialog system which can speak naturally with a human is considered as a major challenge of artificial intelligence. Endto-end dialog system is taken to be a primary research topic in the area of conversational systems. Since an end-to-end dialog system is structured based on learning a dialog policy from transactional dialogs in a defined extent, therefore, useful datasets are required for evaluating the learning procedures. In this paper, different deep learning techniques are applied to the Dialog bAbI datasets [1]. On this dataset, the performance of the proposed techniques is analyzed. The performance results demonstrate that all the proposed techniques attain decent precisions on the Dialog bAbI datasets. The best performance is obtained utilizing end-to-end memory network with a unified weight tying scheme (UN2N).

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“…Arti¯cial neural networks have many applications in di®erent¯elds. [1][2][3][4][5][6][7][8][9][10][11] Fuzzy neural network with various properties such as learning ability, generalization as well as *Corresponding author. This is an Open Access article published by World Scienti¯c Publishing Company.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks

Jafari

Razvarz

Gegov

2020

Vietnam J. Comp. Sci.

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Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.

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Numerical methods for solving fuzzy equations: A survey

Cited by 25 publications

References 57 publications

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

End-to-End Memory Networks: A Survey

A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks

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