Using Fuzzy Measures to Construct Multi-criteria Decision Functions

Fuzzy Measures and Choquet Integrals based on Fuzzy Covering Rough Sets

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2021.3081916 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaohong Zhang ◽

Jingqian Wang ◽

Jianming Zhan ◽

Jianhua Dai

Keyword(s):

Rough Sets ◽

Fuzzy Measures ◽

Choquet Integrals

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Regional integrated energy system schemes selection based on risk expectation and Mahalanobis-Taguchi system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-190211 ◽

2021 ◽

pp. 1-18

Author(s):

Jiahang Yuan ◽

Yun Li ◽

Xinggang Luo ◽

Lingfei Li ◽

Zhongliang Zhang ◽

...

Keyword(s):

Energy Demand ◽

Choquet Integral ◽

Subjective Evaluation ◽

Risk Attitude ◽

Energy System ◽

Energy Utilization ◽

Utilization Efficiency ◽

Energy Structure ◽

Fuzzy Measures ◽

Integrated Energy System

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

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Dynamic image sequence analysis using fuzzy measures

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/3477.938260 ◽

2001 ◽

Vol 31 (4) ◽

pp. 557-572 ◽

Cited By ~ 17

Author(s):

K.C. Tan ◽

T.H. Lee ◽

D. Khoo ◽

E.F. Khor

Keyword(s):

Sequence Analysis ◽

Image Sequence ◽

Image Sequence Analysis ◽

Fuzzy Measures ◽

Dynamic Image

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An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001497000317 ◽

1997 ◽

Vol 11 (05) ◽

pp. 717-734 ◽

Cited By ~ 1

Author(s):

J. M. Westall ◽

M. S. Narasimha

Keyword(s):

Neural Networks ◽

Production System ◽

Background Noise ◽

Evolutionary Approach ◽

Feed Forward ◽

Feed Forward Neural Networks ◽

Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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