• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.351-360
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• 2006

In this paper, we develop an inexact-Newton method for solving nonsmooth operator equations in infinite-dimensional spaces. The linear convergence and superlinear convergence of inexact-Newton method under some conditions are shown. Then, we characterize the order of convergence in terms of the rate of convergence of the relative residuals. The present inexact-Newton method could be viewed as the extensions of previous ones with same convergent results in finite-dimensional spaces.

• Bulletin of the Korean Mathematical Society
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• v.40 no.2
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• pp.235-241
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• 2003

It is proved that the product of any two linearly independent meromorphic solutions of second order linear differential equations with coefficients of small lower growth must have infinite exponent of convergence of its zero-sequences, under some suitable conditions.

• Honam Mathematical Journal
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• v.36 no.3
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• pp.679-688
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• 2014

The notion of asymptotically negative dependence for collection of random variables is generalized to a Hilbert space and the almost sure convergence for these H-valued random variables is obtained. The result is also applied to a linear process generated by H-valued asymptotically negatively dependent random variables.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.550-554
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• 1992

In this paper the issue of convergence rate is introduced for a learning control scheme we have developed and applied for tracking of unknown linear systems. A sufficient condition under which the output trajectory converges exponentially fast is obtained using the controllability grammian of controllable linear systems. Under the same condition it is also shown that the learning control input converges exponentially with the same rate as the rate of output convergence. A numerical example with computer simulation results is presented to show the feasibility of the scheme.

• Honam Mathematical Journal
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• v.46 no.2
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• pp.291-306
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• 2024

The concept of lacunary statistical convergence of triple sequences with respect to gradual 2-normed linear spaces is introduced in this research. We learn about its link to some inclusion and fundamental properties. The notion of lacunary statistical Cauchy triple sequences is introduced in the conclusion, and it is demonstrated that it is equivalent to the idea of lacunary statistical convergence.

• Journal of the Chungcheong Mathematical Society
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• v.23 no.1
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• pp.127-136
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• 2010

For the maximum partial sum of linear process generated by a doubly infinite sequence of identically distributed $\rho$-mixing random variables with mean zeros, a complete convergence is obtained under suitable conditions.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.80-86
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• 2002

A neural network structure which is able to perform the operations of analog addition and linear equation is proposed. The network employs Hopfkeld's model of a neuron with the connection elements specified on the basis of an analysis of the energy function. The analog addition network and linear equation network are designed by using Hopfield's A/D converter and linear programming respectively. Simulation using Pspice has shown convergence predominently to the correct global minima.

• Honam Mathematical Journal
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• v.43 no.1
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• pp.47-58
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• 2021

In this paper, we introduce rough statistical convergence of difference double sequences in normed linear spaces as an extension of rough convergence. We define the set of rough statistical limit points of a difference double sequence and analyze the results with proofs.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.313-321
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• 2020

In this paper, two types of linear actuation methods for the previously proposed adaptive gripper are presented, which includes actual parallelogram inside a five-bar mechanism and has the advantages of smaller actuation torque and larger stroke over the commercial adaptive gripper by RobotiQ. The forward/inverse kinematics and statics analyses for two types of linear actuations are derived. From the inverse kinematics and statics analyses, linear actuation type I is selected and the gripper prototype is designed.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.