• Journal of the Korean Institute of Intelligent Systems
• /
• v.6 no.2
• /
• pp.26-35
• /
• 1996

In this paper, some fundamental problems concerning RBF(radial-basis-function) networks and approximation of functions are addressed. First, a comprehensive introduction to RBF networks is given with typical RBF networks classified into three classes. Next, sharp conditions are given under which continuous functions of a finite number of real variables can be approximated arbitrarily well by a certain class of RBF networks. Finally, a related result is given concerning the representation of functions in the form of distributed RBF networks.

• Nonlinear Functional Analysis and Applications
• /
• v.28 no.3
• /
• pp.647-657
• /
• 2023

The aim of this paper is to study certain subclass ${\tilde{S^q_{\Sigma}}}({\lambda},\,{\alpha},\,t,\,s,\,p,\,b)$ of analytic and bi-univalent functions which are defined by using symmetric q-derivative operator. We estimate the second and third coefficients of the Taylor-Maclaurin series expansions belonging to the subclass and upper bounds for Feketo-Szegö inequality. Furthermore, some relevant connections of certain special cases of the main results with those in several earlier works are also pointed out.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.1 no.1
• /
• pp.85-90
• /
• 1991

After we introduce the properties of bent functions satisfying the SAC(Strict Avalanche Criterion), we made cldar the relationship between two functions, i.e., all Boolean functions satisfying the maximum order SAC. Bant function will be useful to implement cryptographic functions like S-boxes of block cipher, nonlinear combiners, etc. But due to thear 0/1 unbalance and their existence for only even number of input bits, bent functions have some restrictions to use as a building block for constructing bijective cryptographic functions.

• Nonlinear Functional Analysis and Applications
• /
• v.27 no.1
• /
• pp.23-44
• /
• 2022

In this paper, we consider and introduce some new concepts of the biconvex functions involving an arbitrary bifunction and function. Some new relationships among various concepts of biconvex functions have been established. We have shown that the optimality conditions for the general biconvex functions can be characterized by a class of bivariational-like inequalities. Auxiliary principle technique is used to propose proximal point methods for solving general bivariational-like inequalities. We also discussed the conversance criteria for the suggested methods under pseudo-monotonicity. Our method of proof is very simple compared with methods. Several special cases are discussed as applications of our main concepts and results. It is a challenging problem to explore the applications of the general bivariational-like inequalities in pure and applied sciences.

• Bulletin of the Korean Mathematical Society
• /
• v.59 no.6
• /
• pp.1327-1337
• /
• 2022

Zero-difference balanced (ZDB) functions can be applied to many areas like optimal constant composition codes, optimal frequency hopping sequences etc. Moreover, it has been shown that the image set of some ZDB functions is a regular partial difference set, and hence provides strongly regular graphs. Besides, perfect nonlinear functions are zero-difference balanced functions. However, the converse is not true in general. In this paper, we use the decomposition of cyclotomic polynomials into irreducible factors over 𝔽_p, where p is an odd prime to generalize some recent results on ZDB functions. Also we extend a result introduced by Claude et al. [3] regarding zero-difference-p-balanced functions over 𝔽_pⁿ. Eventually, we use these results to construct some optimal constant composition codes.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.11
• /
• pp.127-139
• /
• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.187-190
• /
• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.47 no.5
• /
• pp.17-26
• /
• 2005

The objectives of this study are to determine the behavior of simply supported skew RC slabs subjected to a point load. The p-version nonlinear skew RC FE model has been used. Integrals of Legendre polynomials are used for shape functions with p-level varying from 1 to 10. In the nonlinear formulation of this model, the material model is based on the Kupfer's yield criterion, hardening rule, and crushing condition and layered model is used through the thickness. The cracking behavior is modeled by a smeared crack model and the fixed crack approach is adopted as the crack model. It is shown that the proposed model is able to adequately predict the deflection and ultimate load of nonlinear skew RC slabs with respect to steel arrangements and steel ratios.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.15-38
• /
• 2018

Negative stiffness dampers (NSDs) have been proven an efficient solution to vibration control of stay cables. Although previous studies usually assumed a linear negative stiffness behavior of NSDs, many negative stiffness devices produce negative stiffness with nonlinear behavior. This paper systematically evaluates the impact of nonlinearity in negative stiffness on vibration control performance for stay cables. A linearization method based on energy equivalent principle is proposed, and subsequently, the impact of two types of nonlinear stiffness, namely, displacement hardening and softening stiffness, is evaluated. Through the Hilbert transform (HT) of free vibration responses, the effects of nonlinear stiffness of an NSD on the modal frequencies, damping ratios and frequency response functions of a stay cable is also investigated. The HT analysis results validate the accuracy of the linearization method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.26.6-26
• /
• 2001

In this paper we introduce a new algorithm that enables radial basis function network(RBFN) to be structured automatically and guarantees the stability of the RBFN. Because this new algorithm is efficient and also have the advantage of fast computational speed we adopt this algorithm as online learning scheme for uncertain nonlinear dynamical systems. Based on the fact that a 3-layered RBFN can represent a specific nonlinear function reasonably well by linearly combining a set of nonlinear and localized basis functions, we show that this RBFN can identify the nonlinear system very well without knowing the information of the system in advance.