• 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.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.41-44
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• 1987

Recently Hopfield proposed a model for content-addressable memory, which has been shown to be capable of storing information in a distributed fashion and determining the nearest-neighbor. Its application is, however, inherently limited to the case that the number of l's in each stored vector is nearly the same as the number of O's in that vector. If not the case, the model has high probability of failure in finding the nearest-neighbor. In this work, a modification of the Hopfield's model, which works well irrespective of the number of l's (or O's) in each stored vector, is suggested.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.60-72
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• 1996

In this paper, a enw model-base three-dimensional (3-D) object recognition mehtod using hopfield network is proposed. To minimize deformation of feature values on 3-D rotation, we select 3-D shape features and 3-D relational features which have rotational invariant characteristics. Then these feature values are normalized to have scale invariant characteristics, also. The input features are matched with model features by optimization process of hopjfield network in the form of two dimensional arrayed neurons. Experimental results on object classification and object matching with the 3-D rotated, scale changed, an dpartial oculued objects show good performance of proposed method.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.34-37
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• 2002

This paper presents a new stochastic binary neural network based on the Hopfield model. We apply the proposed network to TSP and compare it with other methods by computer simulations. Furthermore, we apply 2-opt to the proposed network to improve the performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.143-146
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• 1996

In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1576-1583
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• 1989

In this paper, we describe real-time optical implementation of the Hopfield neural network model for two-dimensional associative memory by using commercial LCTV and Multifocus For real-time processing capability, we use LCTV as a memory mask and a input spatial light modulator. Inner product between input pattern and memory matrix is processed by the multifocus holographic lens. The output signal is then electrically thresholded fed back to the system input by 2-D CCD camera. From the good experimental results, the proposed system can be applied to pattern recognition and machine vision in future.

• 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.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.157-163
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• 1998

A neural network structure able to perform the operations of analogue and binary addition is proposed. The network employs Hopfield' model of a neuron with the connection elements specified on the basis of an analysis of the energy function. Simulation using NMOS neurons has shown convergence predominantly to the correct global minima.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.163-170
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• 2016

Tropospheric delay is one of the largest error source in pseudolite navigation system. Because a pseudolite is installed on the ground and transmits its signal to a user in the air or on the ground, the conventional tropospheric delay model developed for a satellite navigation doesn't work properly. In this paper, performance analysis of several pseudolite tropospheric delay models has been done using meteorological data. Based on the result, a new compensation method for Hopfield model has been proposed.