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The so-called PageRank algorithm has been used at Google for properly ranking search results. It quantifies the importance of each page by the structure of links in the web. In our recent work, we have proposed a distributed randomized approach for the PageRank computation, where the pages find their own values by communicating with linked pages. This paper builds upon this approach to improve the...
Many researchers have proposed portfolio models based on the stochastic and fuzzy approaches until now, and there are some models considering both random and ambiguous conditions, particularly using fuzzy random or random fuzzy variables. However, few studies with multiobjective random fuzzy models for the portfolio selection problems have been performed. Therefore, a multiobjective random fuzzy portfolio...
The Google search engine employs the so-called PageRank algorithm for ranking the search results. This algorithm quantifies the importance of each Web page based on the link structure of the Web. In this paper, we continue our recent work on distributed randomized computation of PageRank, where the pages locally determine their values by communicating with linked pages. In particular, we propose a...
The self-organizing map that Kohonen has proposed maps high-dimensional vector data to low-dimensional space by phase conservation. And, it generates the feature map that visually catches the similarity among data. In addition, the reference vector where the unit in a competitive layer of SOM is achieved can interpolate an intermediate vector of the input vector data. In the pattern recognition of...
This paper considers linear programming problems where each coefficient of the objective function is expressed by a random fuzzy variable. New decision making models are proposed based on stochastic and possibilistic programming in order to maximize both of possibility and probability with respect to the objective function value. It is shown that each of the proposed models is transformed into a deterministic...
Single input rule modules connected fuzzy inference method (SIRMs method, for short) by Yubazaki can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Seki et al. have proposed functional type single input rule modules connected fuzzy inference method (functional type SIRMs method, for short) which generalizes the consequent part of SIRMs method...
Fuzzy inference has played significant role in many applications. Although simplified fuzzy inference method is currently mostly used, the problem is that the number of fuzzy rules becomes very huge and so the setup and adjustment of fuzzy rules become difficult. On the other hand, Yubazaki et al. have proposed ldquosingle input rule modules connected fuzzy inference methodrdquo (SIRMs method, for...
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