On a new distance measure of three-parameter interval numbers and its application to pattern recognition

2021 ◽  
Author(s):  
Xingxing He ◽  
Yingfang Li ◽  
Keyun Qin
Keyword(s):  
Pattern Recognition ◽  
Distance Measure ◽  
Interval Numbers ◽  
Parameter Interval

A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information

2013 ◽  
Vol 444-445 ◽  
pp. 676-680
Author(s):  
Li Guo ◽  
Guo Feng Liu ◽  
Yu E Bao
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Interval Numbers ◽  
Fuzzy Partition ◽  
Multiple Attribute ◽  
Attribute Information ◽  
Fcm Clustering ◽  
Attribute Clustering ◽  
Interval Valued

In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a FCM clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm. Keywords: interval-valued vector; FCM clustering algorithm; distance measure; fuzzy partition

Multi-attribute grey target decision method with three-parameter interval grey number

2016 ◽  
Vol 6 (2) ◽  
pp. 270-280 ◽  
Author(s):  
Ye Li ◽  
Shanli Zhu ◽  
San-dang Guo
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Ranking Method ◽  
Content Type ◽  
Uncertain Environment ◽  
Decision Method ◽  
Multi Attribute Decision Making ◽  
Parameter Interval ◽  
Grey Number ◽  
Bull's Eye

Purpose – The purpose of this paper is to propose the grey target decision method based on three-parameter interval grey number for dealing with multi-attribute decision-making problems under uncertain environment. Design/methodology/approach – First, the kernel and ranking method of three-parameter interval grey number are defined, which is the basis of determining the positive and negative bull’s-eye. Next, a new distance measure of three-parameter interval grey number is defined in view of the importance of the “center of gravity” point. Furthermore, a new comprehensive bull’s-eye distance is proposed based on the kernel which integrates the distance between different attributes to the positive and negative bull’s-eye. Then attribute weights are obtained by comprehensive bull’s-eye distance minimum and grey entropy maximization. Findings – The paper provides a grey target decision method based on three-parameter interval grey number and example analysis shows that the method proposed in this paper is more reasonable and effective. Research limitations/implications – If we have a better understanding of the distribution characteristics of three-parameter interval grey number, it is possible to have a more reasonable measure of the distance of three-parameter interval grey number. Practical implications – The paper provides a grey target decision method, which can help decision maker deal with multi-attribute decision-making problems under uncertain environment. Originality/value – This paper proposed the kernel and ranking method of three-parameter interval grey number, and defined a new distance measure of three-parameter interval grey number and proposed a new comprehensive bull’s-eye distance, Furthermore, this paper structured a grey target decision method based on three-parameter interval grey number.

Improved distance measure for pattern recognition

1971 ◽  
Vol 7 (18) ◽  
pp. 521 ◽  
Author(s):  
B.G. Batchelor
Keyword(s):  
Pattern Recognition ◽  
Distance Measure

Ordered weighted logarithmic averaging distance-based pattern recognition for the recommendation of traditional Chinese medicine against COVID-19 under a complex environment

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuhe Fu ◽  
Chonghui Zhang ◽  
Yujuan Chen ◽  
Fengjuan Gu ◽  
Tomas Baležentis ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Recommendation System ◽  
Distance Measure ◽  
Uncertain Information ◽  
Content Type ◽  
Appropriate Treatment ◽  
Treatment Plans ◽  
Fuzzy Linguistic

PurposeThe proposed DHHFLOWLAD is used to design a recommendation system, which aims to provide the most appropriate treatment to the patient under a double hierarchy hesitant fuzzy linguistic environment.Design/methodology/approachBased on the ordered weighted distance measure and logarithmic aggregation, we first propose a double hierarchy hesitant fuzzy linguistic ordered weighted logarithmic averaging distance (DHHFLOWLAD) measure in this paper.FindingsA case study is presented to illustrate the practicability and efficiency of the proposed approach. The results show that the recommendation system can prioritize TCM treatment plans effectively. Moreover, it can cope with pattern recognition problems efficiently under uncertain information environments.Originality/valueAn expert system is proposed to combat COVID-19 that is an emerging infectious disease causing disruptions globally. Traditional Chinese medicine (TCM) has been proved to relieve symptoms, improve the cure rate, and reduce the death rate in clinical cases of COVID-19.

Application of Syntactic Pattern Recognition Approach in Design and Optimisation of Group Machining Systems

2010 ◽  
Vol 165 ◽  
pp. 342-347 ◽  
Author(s):  
Mieczyslaw Siemiatkowski
Keyword(s):  
Pattern Recognition ◽  
Group Process ◽  
Clustering Algorithm ◽  
Group Technology ◽  
Distance Measure ◽  
Cell Formation ◽  
Systems Design ◽  
Syntactic Pattern Recognition ◽  
Job Sequencing ◽  
Syntactic Pattern

The focus of this paper is on planning applications of group technology (GT) and the design of related layouts for multi-assortment cellular manufacturing (CM) of mechanical parts. A methodical approach is developed to optimally solve cell formation (CF) problems with CM systems design, which consists in the identification of machine cells and corresponding part families. The approach involves the use of syntactic pattern recognition concepts from the field of artificial intelligence (AI). It is based on methods of strings matching and clustering, applied extensively in genetics, molecular chemistry and biological sciences. The CF strategy followed implies clustering character strings that denote machine sequences in process routings. Numerical quantification of dissimilarity between part routings by a specific distance measure and the concept of average linkage clustering algorithm (ALCA) are at the core of the clustering procedure. The use of the approach is studied numerically with regard to a real industrial case and diverse layouts of cellular system are considered, including those with machine sharing. Group process alternatives with given system layouts and workflows prototyped by definite job sequencing rules, are simulated using programmed models. Generated design solutions are subjected to further analysis and quantitative evaluation by assumed measures of their operational performance.

Relative Distance Measure Model for the Fuzzy Synthetic Evaluation of Subgrade Stability in Seasonal Frozen Area

2012 ◽  
Vol 178-181 ◽  
pp. 1213-1217
Author(s):  
Han Bing Liu ◽  
Yi Ming Xiang ◽  
Hui Wang ◽  
Yan Yi Sun
Keyword(s):  
Structural Reliability ◽  
Distance Measure ◽  
Simulation Method ◽  
Relative Distance ◽  
Fuzzy Synthetic Evaluation ◽  
Interval Numbers ◽  
Evaluation Indexes ◽  
Measure Model ◽  
Grading Standards ◽  
Synthetic Evaluation

Based on the fuzziness and uncertainty of the subgrade stability in seasonal frozen area, the relative distance measure model with evaluation indexes and weights in the form of interval numbers is presented for the fuzzy synthetic evaluation of the subgrade stability. Firstly, the relative distance measure of each single index between the evaluated subgrade stability and the grading standards is defined. Then, the fuzzy synthetic evaluation model, which considers the functionality and proportionality of evaluation indexes, is established to calculate the comprehensive relative distance measure by using the Monte Carlo simulation method and the sequential relation analysis. Finally, a new decision index of the comprehensive relative distance measure is defined considering the concept of structural reliability, and the stability grade of seasonal frost soil subgrade can be determined by the minimum decision index from the corresponding grading standards. A practical example is given to demonstrate the feasibility and practicability of the proposed model.

scholarly journals Extension Dependent Degree Method with Mapping Transformation for Three-Parameter Interval Number Decision Making

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Li-Bo Xu ◽  
Xing-Sen Li ◽  
Jun-kai Shao ◽  
Kai-jie Wang
Keyword(s):  
Decision Making ◽  
Interval Mapping ◽  
Transformation Method ◽  
Interval Number ◽  
Interval Numbers ◽  
New Approach ◽  
Dependent Function ◽  
Optimal Value ◽  
Decision Making Problem ◽  
Parameter Interval

In view of the multiattribute decision making problem that the attribute values and weights are both three-parameter interval numbers, a new decision making approach and framework based on extension simple dependent degree are proposed. According to traditional extension simple dependent function, the new approach proposes a new extension dependent function for three-parameter interval number. Then through an interval mapping transformation method, the process for obtaining dependent degree for the interval with its optimal value not being the endpoint is transformed to the monotonous process for the interval with its optimal value being the endpoint. The method can not only perform uncertain analysis of decision results by different settings of attitude coefficients, but also take dynamic analysis and rule finding by some extension transformation. At last, an example is presented to examine the effectiveness and stability of our method.

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