• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.87-91
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• 2001

This paper discusses the influence of input conductance on the convergece of the continuous Hopfield neural networks. The convergence has been analyzed for the input and output nodes of neurons. Also, the characteristics of equilibrium points has been analyzed depending on different values of the input conductance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.339-347
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• 1993

The channel assignment problem in a mobile communication system is a NP-complete combinatorial optimization problem, in which the calculation time increases exponentially as the range of the problem is extended. This paper adapts a conventional Hopfield neural network model to the channel assignment problem to relieve the calculation time by means of the parallelism supplied from the neural network. In the simulation study, we checked the feasability of such a parallel method for the fixed channel assignment with uniform, and nouniform channel requirements, and for the dynamic channel assignment with considering continously varying channel requirements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.561-570
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• 1989

In this paper, we describe the theoretical analysis and optical implementation of real-time associative memory based on the modified Hopfield neural network model by using a commerical LCTV connected to computer graphic as the real-time memory mask and adding one mask line to the momory mask in order to optically obtain the time-varing thresholding values of the modified Hopfield model.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.174-179
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• 1999

In this paper, we propose an optimal algorithm for finding the shortest connection of two kinds of parts to pairs. If total part numbers are of size N, then there are order 2ㆍ(N/2)$^{N}$ possible solutions, of which we want the one that minimizes the energy function. The appropriate dynamic rule and parameters used in network are proposed by a new energy function which is minimized when 3-constraints are satisfied. This dynamic nile has three important parameters, an enhancement variable connected to pairs, a normalized distance term and a time variable. The enhancement variable connected to pairs have to a perfect connection of two kinds of parts to pairs. The normalized distance term get rids of a unstable states caused by the change of total part numbers. And the time variable removes the un-optimal connection in the case of distance constraint and the wrong or not connection of two kinds of parts to pairs. First of all, we review the theoretical basis for Hopfield model and present a new energy function. Then, the connection matrix and the offset bias created by a new energy function and used in dynamic nile are shown. Finally, we show examples through computer simulation with 20, 30 and 40 parts and discuss the stability and feasibility of the resultant solutions for the proposed connection algorithm.m.

• Structural Engineering and Mechanics
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• v.7 no.5
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• pp.485-502
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• 1999

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.329-331
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• 2000

데이터 클러스터링은 서로 유사한 성질을 갖는 데이터들은 동일한 클러스터에 분류하고, 이질적인 데이터는 다른 클러스터에 분류하여, 클러스터 내의 유사성은 최대로 하고 클러스터와 클러스터사이의 유사성을 최소로 하는 것을 말한다. 데이터 클러스터링은 데이터 마이닝, 기계 학습, 패턴 인식, 통계 분야 등에 다양하게 활용되고 있다. Hopfield 네트워크는 조합적 최적화 문제를 해결하는데 사용되어 좋은 결과를 나타내고 있다. 본 논문에서는 Hopfield 네트워크를 사용하여 데이터 클러스터링 문제를 해결하는 알고리즘을 연구하였고, 실험을 통해 기존의 방법과 비교하였다.

• Journal of the Chungcheong Mathematical Society
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• v.34 no.3
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• pp.221-234
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• 2021

We introduce high-order Hopfield neural networks with Leakage delays. Furthermore, we study the uniqueness and existence of Hopfield artificial neural networks having the weighted pseudo almost periodic forcing terms on finite delay. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.434-439
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• 1994

Recently neural networks leave been proposed as new computational tools for solving constrained optimization problems because of its computational power. In this paper, the shortest path finding algorithm is proposed by rising a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defined at first. To obtain this energy function, the concept of a vector-represented network is introduced to describe the connected path. Through computer simulations, it will be shown that the proposed algorithm works very well in many cases. The local minima problem of a Hopfield type neural network is discussed.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.63-68
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• 2018

Interests in face recognition area have been increasing due to diverse emerging applications. Face recognition algorithm from a two-dimensional source could be challenging in dealing with some circumstances such as face orientation, illuminance degree, face details such as with/without glasses and various expressions, like, smiling or crying. Hopfield Network capabilities have been used specially within the areas of recalling patterns, generalizations, familiarity recognitions and error corrections. Based on those abilities, a specific experimentation is conducted in this paper to apply the Redundant Parallel Hopfield Network on a face recognition problem. This new design has been experimentally confirmed and tested to be robust in any kind of practical situations.