• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1364-1371
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• 1990

We propose a time-optimal path planning scheme for the robot manipulator using Hopfield neural network. The time-optimal 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 networke technique, which can allow the parallel computation, to the minimum time problem. This paper proposes an approach for solving the time-optimal path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using a PUMA 560 manipulator.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.571-571
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• 1995

• 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.

• 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.

• The Journal of Information Technology
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• v.7 no.2
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• pp.43-54
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• 2004

Since Dr. John J. Hopfield has proposed the HOpfield network, it has been widely applied to the pattern recognition and the routing optimization. The method of Jian-Hua Li improved efficiency of Hopfield network which input pattern's weights are regenerated by SVD(singluar value decomposition). This paper deals with Li's Hopfield Network by linear pre-processing. Linear pre-processing is used for increasing orthogonality of input pattern set. Two methods of pre-processing are used, Hadamard method and random method. In manner of success rate, radom method improves maximum 30 percent than the original and hadamard method improves maximum 15 percent. In manner of success time, random method decreases maximum 5 iterations and hadamard method decreases maximum 2.5 iterations.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.43-44
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• 2019

본 논문에서는 머신러닝 알고리즘 중 하나인 Hopfield Network 알고리즘을 이용하여 SDN 환경에서 분산된 컨트롤러를 선택하는 모델을 제안하였다. Hopfield Network 알고리즘은 신경망의 물리적 모델로써 최적화, 연상기억 등에 사용되는데 이를 통해 효율적인 컨트롤러 동기화를 기대한다.

• 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.

• 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.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.832-834
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• 1996

This paper presents the research on the application of the Hopfield Neural Network to the Economic Load Dispatch problem. The ELD problem has convex cost functions as the objective functions, power balance equation and real power lower/upper limits as the constraints. So we have shown that the possibility of the application of the Hopfield Neural Network to the ELD problem. Through the case study, the simulation results are very close to the numerical method and the dynamic programming method.