• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.4
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• pp.251-259
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• 2008

This paper presents the hardware design of a 32bit floating point based processor. The processor can perform nonlinear functions such as sinusoidal functions, exponential functions, and other mathematical functions. Using the Taylor series and Newton - Raphson method, nonlinear functions are approximated. The processor is actually embedded on an FPGA chip and tested. The numerical accuracy of the functions is compared with those computed by the MATLAB and confirmed the performance of the processor.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.74-76
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• 2007

This paper presents the hardware design of a 32bit floating point based processor. The processor can perform nonlinear functions such as sinusoidal functions, exponential functions, and other nonlinear functions. Using the Taylor series and the Newton - Raphson method, nonlinear functions are approximated. The processor is actually embedded on an FPGA chip and tested. The numerical accuracy of the functions is compared with those computed by the MATLAB.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1021-1024
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• 2000

In this paper, a neuro-fuzzy system for quantitative characterization of chaotic signals is proposed. The proposed system is differ from the previous methods in that the nonlinear functions of the nonlinear dynamical systems are calculated as the invariant factor. In the proposed neuro-fuzzy system, the nonlinear functions are determined by supervised learning. From the reconstructed nonlinear functions, the proposed system can generate extrapolated chaotic signals. This feature will help the study of nonlinear dynamical systems which require large number of chaotic data. To confirm the validity of the proposed system, nonlinear functions are reconstructed from 1-dimensional and 2-dimensional chaotic signals.

• Nonlinear Functional Analysis and Applications
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• v.26 no.5
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• pp.1077-1089
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• 2021

In this article, we introduce the quasi strongly E-convex function and pseudo strongly E-convex function on strongly E-convex set which generalizes strongly E-convex function defined by Youness [10]. Some non trivial examples have been constructed that show the existence of these functions. Several interesting properties of these functions have been discussed. An important characterization and relationship of these functions have been established. Furthermore, a nonlinear programming problem for quasi strongly E-convex function has been discussed.

• International Journal of Contents
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• v.10 no.1
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• pp.18-22
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• 2014

Hidden nodes have a key role in the information processing of feed-forward neural networks in which inputs are processed through a series of weighted sums and nonlinear activation functions. In order to understand the role of hidden nodes, we must analyze the effect of the nonlinear activation functions on the weighted sums to hidden nodes. In this paper, we focus on the effect of nonlinear functions in a viewpoint of information theory. Under the assumption that the nonlinear activation function can be approximated piece-wise linearly, we prove that the entropy of weighted sums to hidden nodes decreases after piece-wise linear functions. Therefore, we argue that the nonlinear activation function decreases the uncertainty among hidden nodes. Furthermore, the more the hidden nodes are saturated, the more the entropy of hidden nodes decreases. Based on this result, we can say that, after successful training of feed-forward neural networks, hidden nodes tend not to be in linear regions but to be in saturated regions of activation function with the effect of uncertainty reduction.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.4
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• pp.883-893
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• 2011

In this paper we study h-stability of the solutions of nonlinear difference system via the notion of $n_{\infty}$-summable similarity between its variational systems. Also, we show that two concepts of h-stability and h-stability in variation for nonlinear difference systems are equivalent. Furthermore, we characterize h-stability for nonlinear difference systems by using Lyapunov functions.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.385-399
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• 2003

We first introduce the quasi-score estimating function and applied the quasi-score estimating function to nonlinear time series models. We proposed the M quasi-score estimating functions bounded functions for the quasi-score estimating functions. Also, we investigated the asymptotic properties of quasi-likelihood estimators and M quasi-likelihood estimators. Simulation results show that the M quasi-likelihood estimators work better than the least squares estimators under the heavy-tailed distributions

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• Steel and Composite Structures
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• v.24 no.4
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• pp.391-397
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• 2017

With the increasing application of explosive composite structure in many engineering fields, its interface bonding state detection is more and more significant to avoid catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, the concept of nonlinear output frequency response functions (NOFRFs) is introduced to detect the bonding state of explosive composite structure. The NOFRFs can describe the nonlinear characteristics of nonlinear vibrating system. Because of the presence of the bonding interface, explosive composite structure itself is a nonlinear system; when bonding interface of the structure is damaged, its dynamic characteristics show enhanced nonlinear characteristic. Therefore, the NOFRFs-based detection index is proposed as indicator to detect the bonding state of explosive composite pipes. The experimental results verify the effectiveness of the detection approach.