• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.5
• /
• pp.877-883
• /
• 2001

In this paper, we propose a new Genetic Algorithm(GA)-based Multiuser Detector(MUD), and its performance is evaluated by computer simulation compared to both the optimum MUD and the Hopfield neural network(NN) -based MUD when the near-far problem exists. From the results of comprehensive simulation, it is shown that the proposed MUD in this paper can guarantee a close BER performance compared to both the optimum MUD and the Hopfield NN MUD with a considerable reduced complexity under the near-far condition. Furthermore, a more performance improvement than the Hopfield W MUD can be expected when the near-far problem does not exist.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.587-591
• /
• 2003

This study develops a neural network for solving optimization problems. Hopfield network has been used for such problems, but it frequently gives abnormal solutions or non-optimal solutions. Moreover, it takes much time for solving a solution. To overcome such disadvantages, this study adopts a neural network whose output nodes change with a small value at every evolution, and the proposed neural network is applied to solve ALB (Assembly Line Balancing) problems . Given a precedence diagram and a required number of workstations, an ALB problem is solved while achieving even distribution of workload among workstations. Here, the workload variance is used as the index of workload deviation, and is reflected to an energy function. The simulation results show that the proposed neural network yields good results for solving ALB problems with high success rate and fast execution time.

• The KIPS Transactions:PartB
• /
• v.13B no.3 s.106
• /
• pp.323-328
• /
• 2006

Proteins in a cell appear as spots in a two dimensional gel image which is used in protein analysis. The spots from the same protein are in near position when comparing two gel images. Finding out the different proteins between a normal tissue and a cancer one is important information in drug development. Automatic matching of gel images is difficult because they are made from biological experimental processes. This matching problem is known to be NP-hard. Neural networks are usually used to solve such NP-hard problems. Hopfield neural network is selected since it is appropriate to solve the gel matching. An energy function with location and distance parameters is defined. The two spots which make the energy function minimum are matching spots and they came from the same protein. The energy function is designed to reflect the topology of spots by examining not only the given spot but also neighborhood spots.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.254-257
• /
• 1993

This paper presents a new method to implement Hebbian learning method on artificial neural network. In hebbian learning algorithm, complexity in terms of multiplications is high. To save the chip area, we consider a new learning circuit. By calculating similarity, or correlation between $X_i$ and $O_i$, large portion of circuits commonly used in conventional neural networks is not necessary for this new hebbian learning circuit named COR. The output signals of COR is applied to weight storage capacitors for direct control the voltages of the capacitors. The weighted sum, ${\Sigma}W_{ij}O_j$, is realized by multipliers, whose output currents are summed up in one line which goes to learning circuit or output circuit. The drain current of the multiplier can produce positive or negative synaptic weights. The pass transistor selects eight learning mode or recall mode. The layout of an learnable six-neuron fully connected Hopfield neural network is designed, and is simulated using PSPICE. The network memorizes, and retrieves the patterns correctly under the existence of minor noises.

• Proceedings of the KIEE Conference
• /
• 1995.11a
• /
• pp.569-571
• /
• 1995

This paper compares the simulated annealing and the Hopfield neural network method for an optimal routing in a multistage interconnection network(MIN). The MIN provides a multiple number of paths for ATM cells to avoid cell conflict. Exhaustive search always finds the optimal path, but with heavy computation. Although greedy method sets up a path quickly, the path found need not be optimal. The simulated annealing can find an sub optimal path in time comparable with the greedy method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.6
• /
• pp.447-453
• /
• 2000

Using the scattering theory of laser light, we analyze the particle sizing method. The scattered profile measured by the photodetector is sampled, scale conditioned by a 32 channel analog-to-digital converter, and is analyzed with the transform matrix from the light energy signals to the weights of the particle sizes. The particle size distribution is classified using the Hopfield neural network method as well as the conventional nonnegative least square method.

• Journal of the Chungcheong Mathematical Society
• /
• v.35 no.1
• /
• pp.39-52
• /
• 2022

We introduce Clifford-valued neural networks with leakage delays. Furthermore, we study the uniqueness and existence of Clifford-valued Hopfield artificial neural networks having the Stepanov weighted pseudo almost periodic forcing terms on leakage delay terms. However the noncommutativity of the Clifford numbers' multiplication made our investigation diffcult, so our results are obtained by decomposing Clifford-valued neural networks into real-valued neural networks. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1117-1119
• /
• 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
• /
• 2002.07b
• /
• pp.790-793
• /
• 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.