• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1553-1556
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.275-278
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• 2001

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.05a
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• pp.52-56
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.505-509
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• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.62-67
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• 2002

From the view point of VLSI implementation, a new teaming algorithm suited for network with quantized connection weights is desired. This paper presents a new teaming algorithm for the DBM(deterministic Boltzmann machine) network with quantized connection weight. The performance of proposed algorithm is tested with the 2-input XOR problem and the 3-input parity problem through computer simulations. The simulation results show that our algorithm is efficient for quantized connection neural networks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.409-412
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• 2000

본 논문에서는 기존의 결정론적 볼츠만 머신의 학습알고리듬을 수정하여 양자화결합을 갖는 볼츠만 머신에도 적용할 수 있는 알고리듬을 제안하였다. 제안한 알고리듬은 2-입력 XOR문제와 3-입력 패리티문제에 적용하여 성능을 분석하였다. 그 결과 하중이 대폭적으로 양자화된 네트워크도 학습이 가능하다는 것은 은닉 뉴런수를 증가시키면 한정된 하중값의 범위로 유지할 수 있다는 것을 보여주었다. 또한 1회에 갱신하는 하중의 개수 m$_{s}$를 제어함으로써 학습계수를 제어하는 효과가 얻어지는 것을 확인하였다..

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.411-415
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• 2003

본 논문에서는 비단조뉴런 모델을 이용한 DBM(Deterministic Boltzmann Machine)네트워크의 학습능력을 평가하였다. 먼저 제안한 네트워크의 은닉층 뉴런수의 변화에 따른 학습성능을 기존의 단조뉴런 모델을 이용한 네트워크의 경우와 비교하였다. 또한 대표적인 학습 모델인 백프로퍼게이션의 경우와도 비교하여 제안한 네트워크가 우수한 성능을 보임을 확인하였다. 마지막으로 네트워크의 응용을 위하여 비단조 DBM 네트워크를 VHDL로 구현하였다.

• 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.