한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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- Pages.275-278
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- 2001
비단조뉴런 DBM 네트워크의 학습 능력에 관한 연구
Learning Ability of Deterministic Boltzmann Machine with Non-Monotonic Neurons
초록
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.
키워드