Dynamic analysis of customer needs using fuzzy Markov chain and fuzzy weighted average methods

2016 ◽  
Author(s):  
C. K. Kwong ◽  
Huimin Jiang ◽  
R. Aydin
Keyword(s):  
Markov Chain ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Customer Needs ◽  
Fuzzy Weighted Average ◽  
Fuzzy Markov Chain

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

THE CURRENT TRENDS AND FACTORS IN THE DEVELOPMENT OF MORTGAGE HOUSING LENDING IN RUSSIA

2020 ◽  
Vol 6 (4) ◽  
pp. 320-333
Author(s):  
Natalia A. BABURINA ◽  
Alexey G. KUTSEV ◽  
Daria D. Mukhametzianova ◽  
Lilia A. Kharitonova
Keyword(s):  
Regression Analysis ◽  
Dynamic Analysis ◽  
Interest Rates ◽  
Residential Buildings ◽  
Weighted Average ◽  
Real Estate Market ◽  
Mortgage Lending ◽  
Russian Scientific ◽  
Statistical Dynamic ◽  
Lending Institutions

The presented work contributes to the development of the studies evaluating the key determinants of mortgage housing lending development in Russia. Despite a relatively well-developed body of research in the field of mortgage housing lending, devoted to revealing the essence, functional purpose, and implementation mechanism, the works, aimed at identifying the factors influencing its development in the current economic environment in Russia, are under-represented in the Russian scientific field. This study aims to assess the dynamics of mortgage housing lending in the contemporary economic conditions and to identify the determinants of its defining. The research methodology is based on the use of statistical dynamic analysis and correlation-regression analysis. The work is based on the application of methods of statistical dynamic analysis and correlation-regression analysis. Assessment of the dynamics has shown the general trend towards an increase in the volume of mortgage housing loans. However, some periods have been noted to have a negative dynamic primary related to the negative external environment and stagnation of the economy, due to the COVID-19 pandemic. Additionally, this article presents the assessment of the qualitative indicators characterizing the level of penetration and the degree of aggressiveness of lending institutions’ policies on housing mortgage lending. The authors have built a panel data model that has allowed identifying the key determinants of the development of residential mortgage lending in Russia. The results have revealed that the state of the mortgage lending sector in Russia is influenced by such factors, as the commissioning of residential buildings, the cost in the primary real estate market, weighted average interest rates, and the unemployment rate.

scholarly journals Induced intuitionistic fuzzy ordered weighted averaging: Weighted average operator and its application to business decision-making

2014 ◽  
Vol 11 (2) ◽  
pp. 839-857 ◽  
Author(s):  
Zeng Shouzhen ◽  
Wang Qifeng ◽  
José Merigó ◽  
Pan Tiejun
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Weighted Averaging ◽  
Business Decision ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Decision Making Problem ◽  
Selection Of

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

Non-stationary fuzzy Markov chain

2007 ◽  
Vol 28 (16) ◽  
pp. 2201-2208 ◽  
Author(s):  
F. Salzenstein ◽  
C. Collet ◽  
S. Lecam ◽  
M. Hatt
Keyword(s):  
Markov Chain ◽  
Fuzzy Markov Chain

Fuzzy weighted average: An improved algorithm

1992 ◽  
Vol 49 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Tian-Shy Liou ◽  
Mao-Jiun J. Wang
Keyword(s):  
Weighted Average ◽  
Fuzzy Weighted Average ◽  
Improved Algorithm

scholarly journals A Markovian-QFD approach in addressing the changing priorities of the customer needs

2016 ◽  
Vol 33 (8) ◽  
pp. 1062-1075 ◽  
Author(s):  
Mehdi Rajabi Asadabadi
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Quality Function Deployment ◽  
Analytic Network Process ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Customer Needs ◽  
Customer Preferences ◽  
Network Process ◽  
Qfd Method

PurposeThe purpose of this paper is to introduce a solution to the problem of changing priorities of customer needs (CNs) in quality function deployment (QFD). Customer preferences and priorities are not very stable and they may change before products are ready for the market. Therefore, finding CNs accurately is a key to reach a higher level of customer satisfaction through improving products. Design/methodology/approachIn the proposed model, a Markov chain is employed to model the changing priorities of CNs. The Markov chain finds a pattern of future CNs, the main inputs of QFD. The QFD method is applied to translate CNs into product requirements (PRs). The analytic network process (ANP) is attached to QFD to ensure that all the relations among the elements, inner and outer, are taken into consideration during the translation process. Thus, CNs are received and adjusted by a Markov chain. FindingsThe application of Markov chains for an ANP-QFD model develops an adequate method of finding a pattern of changing priorities of CNs. This pattern enables the ANP-QFD method to work independent of the initial CNs, and originates a Markovain ANP-QFD. Originality/valueThis study originates a stochastic ANP-QFD model. There have been several papers employing various tools and techniques such as the ANP or analytic hierarchy process for QFD to find accurate relations between PRs and CNs. While there are a few papers applying Markov chains to predict the future of the relations of QFD, there is no study which traces the changes in priorities of the CNs during the improvement process. This is addressed by applying a Markovian ANP-QFD. The model is validated through a case study.

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