• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.1-6
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• 2002

Hopfield introduced the neural network for linear program with linear constraints. In this paper, Hopfield neural network has been generalized to solve the optimization problems including nonlinear constraints. Also, it has been discussed the methods hew to reconcile optimization problem with neural networks and how to implement the circuits.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.160-160
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• 2000

An optimization approach is used to solve the division problem of working area, and a cost function is defined to represent the constraints on the solution, which is then mapped onto the Hopfield neural network for minimization. Each neuron in the network represents a possible combination among many components. Division is achieved by initializing each neuron that represents a possible combination and then allowing the network settle down into a stable state. The network uses the initialized inputs and the compatibility measures among components in order to divide working area.

• Structural Engineering and Mechanics
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• v.7 no.5
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• pp.485-502
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• 1999

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.434-439
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• 1994

Recently neural networks leave been proposed as new computational tools for solving constrained optimization problems because of its computational power. In this paper, the shortest path finding algorithm is proposed by rising a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defined at first. To obtain this energy function, the concept of a vector-represented network is introduced to describe the connected path. Through computer simulations, it will be shown that the proposed algorithm works very well in many cases. The local minima problem of a Hopfield type neural network is discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.120-125
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• 2002

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 KIISE:Information Networking
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• v.29 no.4
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• pp.386-396
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• 2002

This paper presents a neural network-based near-optimal routing algorithm. It employs a modified Hopfield Neural Network (MHNN) as a means to solve the shortest path problem. It uses every piece of information that is available at the peripheral neurons in addition to the highly correlated information that is available at the local neuron. Consequently, every neuron converges speedily and optimally to a stable state. The convergence is faster than what is usually found in algorithms that employ conventional Hopfield neural networks. Computer simulations support the indicated claims. The results are relatively independent of network topology for almost all source-destination pairs, which nay be useful for implementing the routing algorithms appropriate to multi -hop packet radio networks with time-varying network topology.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.5-169
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• 2001

This study proposes a neural network for solving optimization problems such as the TSP (Travelling Salesman Problem), scheduling, and line balancing. The Hopfield network has been used for solving such problems, but it frequently gives abnormal solutions or non-optimal ones. Moreover, the Hopfield network takes much time especially in solving large size problems. To overcome such disadvantages, this study adopts nodes whose outputs changes with a fixed value at every evolution. The proposed network is applied to solving a TSP, finding the shortest path for visiting all the cities, each of which is visted only once. Here, the travelling path is reflected to the energy function of the network. The proposed network evolves to globally minimize the energy function, and a ...

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.157-163
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• 1998

A neural network structure able to perform the operations of analogue and binary addition is proposed. The network employs Hopfield' model of a neuron with the connection elements specified on the basis of an analysis of the energy function. Simulation using NMOS neurons has shown convergence predominantly to the correct global minima.

• 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.