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The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

Xiaojun Wu, Jing Wu, Shangce Gao
2021 Complexity  
The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated  ...  In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution.  ...  describe the experts' evaluation; (3) this study presents a method for solving complex RPN models containing random uncertainty and fuzzy uncertainty with Markov Chain Monte Carlo (MCMC) method. e theoretical  ... 
doi:10.1155/2021/8817667 fatcat:mat6b6iq7fejhflhskhajtzchy

Research on a Sugeno Fuzzy Logic Controller Compared to a Mamdani-Based PI-Type Fuzzy Logic Inference Model

Nguyen Tri, Nguyen Ngoc Khoat
2022 Journal of Science and Technology Issue on Information and Communications Technology  
This paper focuses on creating a theoretical analysis and comparison for both of them.  ...  A typical case study regarding the speed control of a hydropower plant against various load changes is taken into consideration in order to demonstrate the applicability of the proposed control strategy  ...  in theoretical studies but also in real industry.  ... 
doi:10.31130/ud-jst.2022.177ict fatcat:tm6gk4je3fabvgimfp4qvf7pga

A fine grained parallel fuzzy segmentation algorithm on reconfigurable mesh computer

M. Youssfi, O. Bouattane, M. O. Bensalah
2015 Advanced Studies in Theoretical Physics  
The presented classification method is based on a parallel fine grained fuzzy C-means algorithm. It is implemented on a polymorphic SIMD machine to sort out the different components of a brain image.  ...  The use of the massively parallel architecture in the classification domain and particularly for the fuzzy classification is introduced to improve the complexities of the corresponding algorithms.  ...  , 102,150) Table 1 . 1 Comparison of complexities for parallel C-mean and Fuzzy C-mean algorithms.  ... 
doi:10.12988/astp.2015.5111 fatcat:voqsky6qwrbzhhtrejo3ufkru4

Page 2889 of Mathematical Reviews Vol. , Issue 2001E [page]

2001 Mathematical Reviews  
The convergence of the sequences of fuzzy numbers is studied. The properties of the real fuzzy function are discussed.” 2001¢e:03097 03E72 03F52 18B99 Papadopoulos, Basil K.  ...  Furthermore, we show that it is possible to represent any fuzzy relational structure as a Chu space by means of the functor G.”  ... 

MIFuzzy clustering for incomplete longitudinal data in smart health

Hua Fang
2017 Smart Health  
Missing data are common in longitudinal observational and randomized controlled trials in smart health studies.  ...  Multiple-imputation based fuzzy clustering is an emerging non-parametric soft computing method, used for either semi-supervised or unsupervised learning.  ...  With a prespecified number of clusters, its clustering accuracy and inconsistency rates were the poorest in comparison to MI-Fuzzy and K-means.  ... 
doi:10.1016/j.smhl.2017.04.002 pmid:28993813 pmcid:PMC5631546 fatcat:mxcab3w6lrcc7eevzadjqp3bde

Page 446 of Mathematical Reviews Vol. , Issue 86a [page]

1986 Mathematical Reviews  
If A;, 1 < i <k, are fuzzy inputs, B is a fuzzy output, and R is a fuzzy relation describing the system, the above problems are illustrated hy means of a max-min multidimensional equation of type B = A  ...  The authors study the complexity of the operation of performing the product f;f; of two Boolean functions written in disjunctive nor- mal form, with a view to elaborating a strategy for a convenient reduction  ... 

Computational Intelligence in Decision Making

Tianrui Li, Pawan Lingras, Yuefeng Li, Joseph Herbert
2011 International Journal of Computational Intelligence Systems  
The study provides a practical explanation on diverse risk bias decision.  ...  The first paper by Huaxiong Li and Xianzhong Zhou is entitled "Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model".  ...  The authors compare results obtained using K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms.  ... 
doi:10.1080/18756891.2011.9727758 fatcat:k4agqubsmnbuhl5zhowxyhmwiy

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

Sudha V, Girijamma H
2017 International Journal of Advanced Computer Science and Applications  
We believe that this manuscript will give a good summary of the effectiveness of existing clustering techniques towards medical data as a contribution.  ...  However, with the changing time, there is a significant change in forms of the data.  ...  Sulaiman and Isa [36] have presented a technique of image segmentation using fuzzy k-means clustering.  ... 
doi:10.14569/ijacsa.2017.080348 fatcat:qvk7l6tugfemhiqdvfgvuvz6da

Page 39 of Geographical Analysis Vol. 14, Issue 1 [page]

1982 Geographical Analysis  
In the study of way finding, statements like “go a bit up north until you see Cinema X, then turn left and go a long way west” consist of linguistic terms whose spatial meanings are pertinent.  ...  In Fuzzy Sets and their Applications to Cognitive and Decision Processes, edited by L. A. Zadeh, K. S$. Fu, K. Tanaka, and M. Shimura, pp 395-408. New York: Academic Press  ... 

Adaptive Clustering Algorithm of Complex Network Based on Fuzzy Neural Networks

Zhixun Zhang, Juan Wang, Yanqiang Xu, Wei Han, Liping Zhang
2022 Mobile Information Systems  
This study introduces a novel and more effective method for addressing the difficult problems of practical complex control systems.  ...  Clustering analysis, as one of the important methods in data mining technology, merely provides a method for the research and analysis of large amounts of data.  ...  Acknowledgments is study was supported by the Science and Technology Foundation of Gansu Province (Grant no. 18JR3RA228) and Science and Technology Project of Lanzhou (Grant nos. 2018-4-56).  ... 
doi:10.1155/2022/9220581 fatcat:oveq2aw3jjdxvcmol6i6xpcqne

STABILITY OF HYBRID CONTROL SYSTEMS: CRITERIA OF STABILITY AND RISK ANALYSIS

Florin Stanciulescu
2002 IFAC Proceedings Volumes  
The case of a high complexity hydrological system, using a PC 586 or upgrade is illustrated.  ...  A new approach of hybrid control systems, called mathematical-heuristic modelling, and its applications in analysis, simulation and stability of these systems, is presented.  ...  A criterion of stability of hybrid control systems The mathematical-heuristic approach of high complexity systems leads to an important theoretical problem: the stability of systems described by means  ... 
doi:10.3182/20020721-6-es-1901.00515 fatcat:b4vmgtuev5c2zpsyxx4abxdghy

Fractional Refined Composite Multiscale Fuzzy Entropy of International Stock Indices

Zhiyong Wu, Wei Zhang
2019 Entropy  
time series with noise, is proposed to quantify the complexity dynamics of the international stock indices in the paper.  ...  To comprehend the FRCMFE, the complexity analyses of Gaussian white noise with different signal lengths, the random logarithmic returns and volatility series of the international stock indices are comparatively  ...  Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/e21090914 fatcat:sqbfpetlmnboth64uqtpampihi

Page 614 of Mathematical Reviews Vol. 57, Issue 2 [page]

1979 Mathematical Reviews  
Based on this finite number of fuzzy sentences, a method is given for synthesizing a fuzzy grammar that generates the required fuzzy language. K. S. Fu (Lafayette, Ind.)  ...  The inference procedure developed provides a means of synthesizing a probabilistic model of both physical and abstract systems from samples of their behavior. K. S. Fu (Lafayette, Ind.) Maurer, H.  ... 

Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress

Petr Hajek, Ondrej Prochazka
2018 Filomat  
Specifically, we show that this method outperforms FCMs, fuzzy grey cognitive maps and adaptive neuro-fuzzy systems in terms of root mean squared error.  ...  Fuzzy cognitive maps (FCMs) integrate neural networks and fuzzy logic to model complex nonlinear problems through causal reasoning.  ...  In this study, a real-coded GA was used to optimize the weight matrix W={w ji }, j i, of an IVFCM with respect to the RMSE (root mean squared error).  ... 
doi:10.2298/fil1805657h fatcat:t4ruc2zrpnahhopqciqs7co3k4

Cluster validation indices for fMRI data: Fuzzy C-Means with feature partitions versus cluster merging strategies

M.D. Alexiuk, N.J. Pizzi
2004 IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.  
Fuzzy C-Means (FCM) is a standard technique for exploratory analysis and is readily adaptable to integrate unique data characteristics and auxiliary feature relations.  ...  studies.  ...  CONCLUSIONS This initial study has set up a framework for the comparison of fuzzy c-means with partitions (FCMP) to both fuzzy c-means (FCM) and hard cluster merging algorithms.  ... 
doi:10.1109/nafips.2004.1336295 fatcat:v4evgc3csnhetkaoov36ugappm
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