• Bulletin of the Korean Mathematical Society
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• v.34 no.1
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• pp.93-102
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• 1997

Let C be a nonempty closed convex subset of a Banach space E and let $T_1, \cdots, T_N$ be nonexpansive mappings from C into itself (recall that a mapping $T : C \longrightarrow C$ is nonexpansive if $\left\$\mid$ Tx - Ty \right\$\mid$ \leq \left\$\mid$ x - y \right\$\mid$$ for all $x, y \in C$). We consider the fixed point problem for nonexpansive mappings : find a common fixed point, i.e., find a point in $\cap_{i=1}^N Fix(T_i)$, where $Fix(T_i) := {x \in C : x = T_i x}$ denotes the set of fixed points of $T_i$.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.674-678
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• 2007

Baldwin defined the approximate reasoning using truth function mapping. In paper [4], we defined two truth function mappings and applied these truth function mappings to generalized modus ponens. In this paper, we introduce the results of generalized modus tollens using these two truth function mappings.

• East Asian mathematical journal
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• v.28 no.5
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• pp.561-567
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• 2012

The approximate solvability of a generalized system for relaxed cocoercive extended general variational inequalities is studied by using the project operator technique. The results presented in this paper are more general and include many previously known results as special cases.

• Korean Journal of Mathematics
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• v.25 no.1
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• pp.19-35
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• 2017

In this communication, we introduce an Ishikawa type iterative algorithm for finding the approximate solutions of a class of nonlinear set valued variational inclusion problems. We also establish a characterization of strong convergence of this iterative techniques.

• Korean Journal of Mathematics
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• v.26 no.1
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• pp.129-141
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• 2018

In this slush pile, we introduce a new kind of variational inclusions problem stated as random extended generalized nonlinear variational inclusions for random fuzzy mappings. We construct an iterative scheme for the this variational inclusion problem and also discuss the existence of random solutions for the problem. Further, we show that the approximate solutions achieved by the generated scheme converge to the required solution of the problem.

• The Pure and Applied Mathematics
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• v.24 no.2
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• pp.99-107
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• 2017

Shokri et al. [14] proved the Hyers-Ulam stability of bihomomorphisms and biderivations by using the direct method. It is easy to show that the definition of biderivations on normed 3-Lie algebras is meaningless and so the results of [14] are meaningless. In this paper, we correct the definition of biderivations and the statements of the results in [14], and prove the corrected theorems.

• Bulletin of the Korean Mathematical Society
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• v.47 no.1
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• pp.195-209
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• 2010

In this paper, we prove the generalized Hyers-Ulam stability of bi-homomorphisms in $C^*$-ternary algebras and of bi-derivations on $C^*$-ternary algebras for the following bi-additive functional equation f(x + y, z - w) + f(x - y, z + w) = 2f(x, z) - 2f(y, w). This is applied to investigate bi-isomorphisms between $C^*$-ternary algebras.

• East Asian mathematical journal
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• v.26 no.5
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• pp.693-702
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• 2010

We study one-step iteration process to approximate common fixed points of two nonexpansive mappings and prove some convergence theorems in convex metric spaces. Using the so-called condition (A'), the convergence of iteratively defined sequences in a uniformly convex metric space is also obtained.

• The Pure and Applied Mathematics
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• v.22 no.4
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• pp.389-401
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• 2015

We will show the general solution of the functional equation f(x + ay) + f(x − ay) + 2(a² − 1)f(x) = a²f(x + y) + a²f(x − y) + 2a²(a² − 1)f(y) and investigate the stability of quartic Lie *-derivations associated with the given functional equation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.105-108
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• 2001

In this paper, the learning algorithm called advanced expanded and truncate algorithm(AETLA) is proposed to training multilayer binary neural network to approximate binary to binary mapping. AETLA used merit of ETL and MTGA learning algorithm. We proposed to new learning algorithm to decrease number of hidden layer. Therefore, learning speed of the proposed AETLA learning algorithm is much faster than other learning algorithm.