• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.718-724
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• 1991

In this paper, we proposed an algorithm to improve the memory efficiency by means of learning thresholds in spite of correlations among input patterns to be memorized. The proposed algorithm does not need preprocess correlations among input patterns but processes them with a threshold on a neural network. When memory contents are destroyed by correlation, nearly all patterns can be properly recovered with past learning. Through experiments we show how out algorithm can improve the memory efficiency.

• Journal of Welding and Joining
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• v.18 no.3
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• pp.114-121
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• 2000

Since resistance spot welding has become one of the most popular sheet metal fabrication processes, a strong emphasis is being put on the quality of the welds. Throughout the years many quality estimation systems have been developed by many researchers to ensure weld quality. In this study, the process variables, which were monitored in the primary circuit of the welding machine, are used to estimate the weld quality with Hopfield neural network. The primary dynamic resistance is vectorized and stored as five patterns in the network. As the welding is done, the dynamic resistance patterns are recognized and the quality is estimated with the proposed method. Due to the primary process variables, it is possible to utilize this algorithms as an in-process real time quality monitoring system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.4
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• pp.295-300
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• 1990

Inverse kinematic problem is a crucial point for robot manipulator control. In this paper, to implement the Jacobian control technique we used the Hopfield, Tank's neural network. The states of neurons represent joint velocities, and the connection weights are determined from the current value of the Jacobian matirx. The network energy function is constructed so that its minimum corresponds to the minimum least square error. At each sampling time, connection weights and neuron states are updated according to current joint positon. Inverse kinematic solution to the planar redundant manipulator is solved by computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.2
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• pp.183-191
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• 1997

A neural network-based algorithm for the static weapon-target assignment (WTA) problem is Presented in this paper. An optimal WTA is one which allocates targets to weapon systems such that the total expected leakage value of targets surviving the defense is minimized. The proposed algorithm is based on a Hopfield and Tank's neural network model, and uses K x M processing elements called binary neuron, where M is the number of weapon platforms and K is the number of targets. From the software simulation results of example battle scenarios, it is shown that the proposed method has better performance in convergence speed than other method when the optimal initial values are used.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.397-400
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• 1989

This study is to design a method of parameter estimation for a second order linear time invarient system of self-tuning controller utilizing the neural network theory proposed by Hopfield. The result is compared with the other methods which are commonly used in controller theories.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.5
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• pp.59-68
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• 1998

This paper proposes a neural network algorithm for a channel assignment in cellular mobile communications. The proposed algorithm is developed base on hopfield neural network in order to minimize the number of channel without a confliction between cells. To compare the performance of the proposed algorithm, we used seven benchmark problems selected from kunz's and funabiki's papers. Experimental results show that the convergence times are reduced form 27% to 66% compared with Kunz's and funabiki's algorithm and vonvergence rates are improved to 100%.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.183-191
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• 1991

This paper develops a pole-zero assignment self-tuning regulator utilizing the method of a neural network in the plant parameter estimation. An approach to parameter estimation of the plant with a Hopfield neural network model is proposed, and the control characteristics of the plant are evaluated by means of a simulation for a second-order linear time invariant plant. The results obtained with those of Exponentially Weighted Recursive Least Squares(EWRLS) method are also shown.

• 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.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.83-94
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• 2021

The need for recognition of partially occluded objects is increasing in the area of computer vision applications. Occlusion causes significant problems in identifying and locating an object. In this paper, an annealed Hopfield network (AHN) is proposed for detecting threat objects in passengers' check-in baggage. AHN is a deterministic approximation that is based on the hybrid Hopfield network (HHN) and annealing theory. AHN uses boundary features composed of boundary points and corner points which are extracted from input images of threat objects. The critical temperature also is examined to reduce the run time of AHN. Extensive computational experiments have been conducted to compare the performance of the AHNwith that of the HHN.