• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.451-463
• /
• 2010

In this paper, we introduce and study a class of completely generalized quasi-variational inclusions with fuzzy mappings. A new iterative algorithm for finding the approximate solutions and the convergence criteria of the iterative sequences generated by the algorithm are also given. These results of existence, algorithm and convergence generalize many known results.

• Journal of applied mathematics & informatics
• /
• v.29 no.5_6
• /
• pp.1363-1379
• /
• 2011

Halpern iterative algorithm is one of the most cited in the literature of approximation of fixed points of nonexpansive mappings. Different authors modified this iterative algorithm in Banach spaces to approximate fixed points of nonexpansive mappings. One of which is Hu [8] and Yao et al [21] modification of Halpern iterative algorithm for nonexpansive mappings in Banach spaces. It is the purpose of this paper to thoroughly analyze this modification and its convergence conditions. Unfortunately, Hu [8] and Yao et al [21] control conditions imposed on the modified Halpern iterative algorithm to have strong convergence are found to be not sufficient. In this paper, counterexamples are constructed to prove that the strong convergence conditions of Hu [8] and Yao et al [21] are not sufficient. It is also proved that with some additional conditions on the control parameters, strong convergence of the defined iterative algorithm is obtained in different Banach space settings.

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.75-86
• /
• 2010

An iterative algorithm was been studied which can be viewed as an extension of the previously known algorithms for asymptotically nonexpansive mappings. Subsequently, we study the convergence problem of the proposed iterative algorithm for asymptotically nonexpansive mappings under some mild conditions in Banach spaces.

• The Pure and Applied Mathematics
• /
• v.27 no.1
• /
• pp.61-70
• /
• 2020

In this paper, we introduce a new concept of stability of coincidence iterative algorithm for three mappings and derive a new three-step Jungck-type iterative algorithm. And, we prove a stability result and a strong convergence result for the Jungck-type algorithm using the M_J-contractive condition. Our results extend and unify the corresponding ones in [3, 6, 7, 13].

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2761-2781
• /
• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.855-861
• /
• 2002

This paper demonstrates the reconstruction of input signals from only the measured signal for the simulation and endurance test of automobiles. The aim of this research is concerned with input signal reconstruction using various iterative teaming algorithm under the condition of system characteristics. From a linear to nonlinear systems which provides the output signals are estimated in this algorithm which is based on the frequency domain. Our concerns are that the algorithm can assure an acceptable stability and convergence compared to the ordinary iterative learning algorithm. As a practical application, a f car model with nonlinear damper system is used to verify the restoration of input signal especially with a modified iterative loaming algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.4E
• /
• pp.9-13
• /
• 1996

We developed an iterative algorithm which could improve the resolution of reconstructed tomograms having random attenuation patterns and analyzed the limitation of this algorithm. The simple back-and forth propagation algorithm has depth resolution about four wavelengths. An iterative algorithm, based on back-and-forth propagation, can be used to improve the resolution of reconstructed tomograms. We analyzed the wavefield for multi-layered specimen and programmed iterative algorithm using Clanguage. Simulation results show that the images get clearer as the number of iterations increases. Also, unambiguous images can be reconstructed using this algorithm even when the layer separation is only two wavelengths. However, this iteration algorithm comes up with an incorrect solution for the number of projections less than five.

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

• Honam Mathematical Journal
• /
• v.26 no.4
• /
• pp.391-399
• /
• 2004

In this paper, we develop an iterative algorithm for solving a class of nonlinear mixed implicit variational inequalities in Hilbert spaces. The resolvent operator technique is used to establish the equivalence between variational inequalities and fixed point problems. This equivalence is used to study the existence of a solution of nonlinear mixed implicit variational inequalities and to suggest an iterative algorithm for solving variational inequalities. In our results, we do not assume that the mapping is strongly monotone.

• 연구논문집
• /
• s.25
• /
• pp.147-154
• /
• 1995

In this paper, we discussed the convergence and the properties of an iterative algorithm method in order to improve a bimean clustering algorithm. This algorithm that we have discussed choose automatically an optimum threshold as a result of an iterative process, successive iterations providing increasingly cleaner extractions of the object region, The iterative approach of a proposed algorithm is seen to select an appropriate threshold for the low contrast images.