• Title/Summary/Keyword: ${\alpha}$-set

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CLASSIFICATIONS OF (α, β)-FUZZY SUBALGEBRAS OF BCK/BCI-ALGEBRAS

  • Jun, Young Bae;Ahn, Sun Shin;Lee, Kyoung Ja
    • Honam Mathematical Journal
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    • v.36 no.3
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    • pp.623-635
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    • 2014
  • Classications of (${\alpha},{\beta}$)-fuzzy subalgebras of BCK/BCI-algebras are discussed. Relations between (${\in},{\in}{\vee}q$)-fuzzy subalgebras and ($q,{\in}{\vee}q$)-fuzzy subalgebras are established. Given special sets, so called t-q-set and t-${\in}{\vee}q$-set, conditions for the t-q-set and t-${\in}{\vee}q$-set to be subalgebras are considered. The notions of $({\in},q)^{max}$-fuzzy subalgebra, $(q,{\in})^{max}$-fuzzy subalgebra and $(q,{\in}{\vee}q)^{max}$-fuzzy subalgebra are introduced. Conditions for a fuzzy set to be an $({\in},q)^{max}$-fuzzy subalgebra, a $(q,{\in})^{max}$-fuzzy subalgebra and a $(q,{\in}{\vee}q)^{max}$-fuzzy subalgebra are considered.

Implemented Circuits of Fuzzy Inference Engine for Servo Control by using Decomposition of $\alpha$-Level Set ($\alpha$-레벨 집합 분해에 의한 서보제어용 퍼지추론 연산회로 구현)

  • Hong Jeng-pyo;Hong Soon-ill
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.2
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    • pp.90-96
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    • 2005
  • This paper presents hardware scheme of fuzzy inference engine, based on α-level set decomposition of fuzzy sets for fuzzy control of DC servo system. We propose a method which is directly converted to PWM actuating signal by a one body of fuzzy inference and defuzzification. The influence of quantity α-levels on input/output characteristics of fuzzy controller and output response of DC servo system is investigated. It is concluded that quantity α-cut 4 give a sufficient result for fuzzy control performance of DC servo system. The experimental results shows that the proposed hardware method is effective for practical applications of DC servo system.

SOME CUBIC JULIA SETS

  • Lee, Hung-Hwan;Baek, Hun-Ki
    • Journal of applied mathematics & informatics
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    • v.4 no.1
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    • pp.31-38
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    • 1997
  • We figure out geometric properties of the Julia set $J_{\alpha}$ of cubic complex polynomial $C_{\alpha}(z)=z^3 + {\alpha}z(\alpha \epsilon \mathbb{C})$ and the smallest ellipse which surrounds $J_{\alpha}$.

A Note on Fuzzy Support Vector Classification

  • Lee, Sung-Ho;Hong, Dug-Hun
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.133-140
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    • 2007
  • The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.

AN ITERATIVE METHOD FOR SOLVING EQUILIBRIUM PROBLEM FIXED POINT PROBLEM AND GENERALIZED VARIATIONAL INEQUALITIES PROBLEM

  • Zhang, Lijuan;Li, Juchun
    • East Asian mathematical journal
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    • v.27 no.5
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    • pp.527-538
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    • 2011
  • In this paper, we introduce a new iterative scheme for finding a common element of the set of an equilibrium problem, the set of fixed points of nonexpansive mapping and the set of solutions of the generalized variational inequality for ${\alpha}$-inverse strongly g-monotone mapping in a Hilbert space. Under suitable conditions, strong convergence theorems for approximating a common element of the above three sets are obtained.

Jensen's Alpha Estimation Models in Capital Asset Pricing Model

  • Phuoc, Le Tan
    • The Journal of Asian Finance, Economics and Business
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    • v.5 no.3
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    • pp.19-29
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    • 2018
  • This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

HOW THE PARAMETER ε INFLUENCE THE GROWTH RATES OF THE PARTIAL QUOTIENTS IN GCFε EXPANSIONS

  • Zhong, Ting;Shen, Luming
    • Journal of the Korean Mathematical Society
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    • v.52 no.3
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    • pp.637-647
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    • 2015
  • For generalized continued fraction (GCF) with parameter ${\epsilon}(k)$, we consider the size of the set whose partial quotients increase rapidly, namely the set $$E_{\epsilon}({\alpha}):=\{x{\in}(0,1]:k_{n+1}(x){\geq}k_n(x)^{\alpha}\;for\;all\;n{\geq}1\}$$, where ${\alpha}$ > 1. We in [6] have obtained the Hausdorff dimension of $E_{\epsilon}({\alpha})$ when ${\epsilon}(k)$ is constant or ${\epsilon}(k){\sim}k^{\beta}$ for any ${\beta}{\geq}1$. As its supplement, now we show that: $$dim_H\;E_{\epsilon}({\alpha})=\{\frac{1}{\alpha},\;when\;-k^{\delta}{\leq}{\epsilon}(k){\leq}k\;with\;0{\leq}{\delta}<1;\\\;\frac{1}{{\alpha}+1},\;when\;-k-{\rho}<{\epsilon}(k){\leq}-k\;with\;0<{\rho}<1;\\\;\frac{1}{{\alpha}+2},\;when\;{\epsilon}(k)=-k-1+\frac{1}{k}$$. So the bigger the parameter function ${\epsilon}(k_n)$ is, the larger the size of $E_{\epsilon}({\alpha})$ becomes.

ON ANNIHILATOR IDEALS OF A NEARRING OF SKEW POLYNOMIALS OVER A RING

  • Hashemi, Ebrahim
    • Journal of the Korean Mathematical Society
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    • v.44 no.6
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    • pp.1267-1279
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    • 2007
  • For a ring endomorphism ${\alpha}$ and an ${\alpha}-derivation\;{\delta}$ of a ring R, we study relation between the set of annihilators in R and the set of annihilators in nearring $R[x;{\alpha},{\delta}]\;and\;R_0[[x;{\alpha}]]$. Also we extend results of Armendariz on the Baer and p.p. conditions in a polynomial ring to certain analogous annihilator conditions in a nearring of skew polynomials. These results are somewhat surprising since, in contrast to the skew polynomial ring and skew power series case, the nearring of skew polynomials and skew power series have substitution for its "multiplication" operation.