• 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.

• 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.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.130-133
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• 1998

A imput buffer typed ATM switch and an appropriate cell-scheduling algorithm are necessary for avoiding output blocking and internal blocking respectively. The algorithm determining a set of non-blocking data cells from the queues can greatly affect on the switch's throughput as well as the behavior of the queues. In this paper bit pattern optimization combined with the Token method in presented in order to improve the performance of ATM switch. The digital Hopfield neural cell scheduler is designed and used for the maximum numbers of cells in real-time

• 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.532-534
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• 1990

We suggest a new energy function that improves the area complexity of the Hopfield Crossbar Network. Through converting data representation to an encoded format, we reduce the number of nodes of the network, and thus reduce the entire size. We apply this approach to the layer assignment problem, and use the modified delayed self-feedback Hopfield Network. Area complexity of the existing network for layer assignment ploblem is improved from O( $N^2L^2$ ) to O($N^2$(log L)$^2$).

• 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.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1117-1119
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• 1999

This paper presents a new approach for the optimal routing problem of distribution system planning using the well known Hopfield Neural Network(HNN) method. The optimal routing problem(ORP) in distribution system planning(DSP) is generally formulated as combinational mixed integer problem with various equality and inequality constraints. For the exceeding nonlinear characteristics of the ORP most of the conventional mathematical methods often lead to a local minimum. In this paper, a new approach was made using the HNN method for the ORP to overcome those disadvantages. And for this approach, a appropriately designed energy function suited for the ORP was proposed. The proposed algorithm has been evaluated through the sample distribution planning problem and the simulation results are presented.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.790-793
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• 2002

In this paper, dynamic path planning of two mobile robots using a modified Hopfield neural network is studied. An area which excludes obstacles and allows gradually changing of activation level of neurons is derived in each step. Next moving step can be determined by searching the next highest activated neuron. By learning repeatedly, the steps will be generated from starting to goal points. A path will be constructed from these steps. Simulation showed the constructed paths of two mobile robots, which are moving across each other to their goals.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.8
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.