• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.247-251
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• 1990

Hopfield neural network has been applied to the problem of economic load dispatching of electric power(ELD). The optimum values of neuron potentials are represented in terms of large numbers. And the neuron potential converges to the medium values between the limit values of the sigmoid function. In three cases, ELD based upon Hopfield network is formulated, solved and discussed.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.12
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• pp.988-995
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• 2000

각 모듈들의 위치가 배치 알고리즘에 의해 결정된 후에도 모듈들을 종축 또는 횡축을 중심으로 뒤집거나 회전시킴으로써 회로의 효율성과 연결성을 향상시킬 수 있다. 고집적 회로설계의 한 단계인 모듈방향 결정 문제는 모듈간에 연결된 선의 길이의 합이 최소가 되도록 각 모듈의 방향을 결정하는 문제이다. 최근에 평균장 어닐링 방법이 조합적 최적화 문제에 사용되어 좋은 결과를 보여 주고 있다. 평균장 어닐링은 신경회로망의 따른 수렴 특성과 시뮬레이티드 어닐링의 우수한 해를 생성하는 특성이 결합된 방법이다. 본 논문에서는 정규화된 평균장 어닐링을 사용해서 모듈 방향 결정 문제를 해결하였고 실험을 통해 기존의 Hopfield 네트워크 방법과 시뮬레이티드 어닐링과 그 결과를 비교하였다. 시뮬레이티드 어닐링, 정규화된 평균장 어닐링과 Hopfield 네트워크의 총 길이 감소율은 각각 19.86%, 19.85%, 19.03%였으며, 정규화된 평균장 어닐링의 실행 시간은 Hopfield 네트워크보다는 1.1배, 시뮬레이티드 어닐링보다는 11.4배 정도 빨랐다.

• 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.

• 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.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.667-672
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• 2001

An optimization approach is used to partition the field of view. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a field of view and one or multiple objects. Partition is achieved by initializing each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between a field of view and one or multiple objects to find a stable state.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.726-730
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• 1990

In this paper, assuming that local range information is available, a collision-free path planning algorithm for multiple mobile robots is presented by using Hopfield neural optimization network. The energy function of the network is built using the present position and the goal position of each robot as well as its local range information. The proposed algorithm has several advantages such as the effective passing around obstacles with the directional safety distance, the easy implementation of robot motion planning including its rotation, the real-time path planning capability from the totally localized computations of path for each robot, and the adaptivity on arbitrary environment since any special shape of obstacles is not assumed.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.62-68
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• 2000

The Dynamic Channel Assignment have a detect which satisfy lots of conditions. It makes system efficiency depreciate because the Dynamic Channel Assignment executes computation process of several steps that demands lots of time. In this paper, we have proposed a traffic control algorithm which makes simple computation process for improving the detect.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.485-491
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• 1990

We propose a minimum-time path planning soheme for the robot manipulator using Hopfield neural network. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to apply the neural network technique, which can allow the parallel computation, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using the PUMA 560 manipulator.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.