• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.66-74
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• 1999

In this paper, we propose a generalized model of statistical Hopfield neural network applicable to solving the well known NP-Complete problem, TSP. Van Den Bout's method to simplify the energy function through normalization has severe weak points that it does not consider the necessary perturbation effects. In proposed model, the improved energy function is used and 5 kinds of perturbation effects and the ratio between perturbation effects are considered including van Den Bout's 2 kinds and one more kind of Park. Through the simulation of randomly generated distribution of 10-city, it is found that our model shows 90 out of 100 cases reach the optimum and near optimum solution(within 5% error). We show the simulation of the large scale, 30-city and 50-city.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.8
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• pp.667-677
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• 1990

In this paper, optical inplementation of the Hopfield neural network model for two-dimensinal associative memory is described For the real-time processing of two-dimensional images, the commercial LCTVs are used as a memory mask and an input spatical light modulator. A 4-D memory matrix is realized with a 2-D mask of a matrix arrangement and the inner-products between arbitrary input pattern and memory matrix are carried out by using the multifocus hololens. The output image is then electronically thresholded and fed back to the input of the associative memory system by 2-D CCd camera. From the good experimental results for the high error correction capability, the proposed system can be applied to practical pattern recognition and machine vision systems.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.465-469
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas, However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. the other problem of MUSIC and ESPRIT is to require calibrated antennas with uniform features, and are sensitive ti the manufacturing fault and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected Hopfield neural model. Computer simulations show the validity of the proposed algorithm. It follows that the proposed method yields better AOA estimates than MUSIC. Moreover, out method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.6
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• pp.545-555
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• 1991

In this paper, We discuss optical implementation of new optical adaptive patern recognition system based on single layer perception with learning capability and associative memory model having error corrective capability. The single layer perceptron is optically implemented by using 2 D LCTV spatial light modulators through the nonlinear quantization and polarization encoding methods, and 2 D hopfield associative memory is also implemented by using multifocus holographic lens. From some experimental results on classfication of Arabic numbers into even & edd numbers, it is shown that the proposed system can classify the patterns to the right classes correctly even for the partial and erronenous input patterns. Accordingly, the proposed optical adaptive pattern recognition system can be suggested for practical application in the fields of image processing and pattern recognition.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.87-91
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. The other problem of MUSIC and ESRPIT is to require calibrated antennas with uniform features, and are sensitive to the manufacturing facult and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm. The proposed method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• 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
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• v.26 no.6
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• pp.108-114
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• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.55-63
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• 1998

This paper presents a new maching algorithm by Hopfield Neural Network for 3-D object recognition. In the proposed method, a model object is represented by a set of polygons in a single coordinate. And each polygon is described by a set of features; feature attributes. In case of 3-D object recognition, the scale and poses of the object are important factors. So we propose a strategy for 3-D object recognition independently to its scale and poses. In this strategy, the respective features of the input or the model objects are changed to the startegical constants when they are compared with one another. Finally, we show that the proposed method has a robustness through the results of experiments which included the classification of the input objects and the matching sequence to its 3-D rotation and scale.