• Title, Summary, Keyword: optimal temporal frequency selection

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Seismic waveform tomography in the frequency-space domain: selection of the optimal temporal frequency for inversion

  • Yokota Toshiyuki;Matsushima Jun
    • Geophysics and Geophysical Exploration
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    • v.7 no.1
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    • pp.19-24
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    • 2004
  • Frequency-space domain full-wave tomography is a promising technique for delineating detailed subsurface structure with high resolution. However, this method requires criteria for the selection of a set of optimal temporal frequency components, to achieve stability in the sequence of inversion processes together with computational efficiency. We propose a method of selecting optimal temporal frequencies, based on wavenumber continuity. The proposed method is tested numerically and is shown to be able to select an optimal set of frequency components that are sufficient to image the anomalies.

Multi Colony Intensification.Diversification Interaction Ant Reinforcement Learning Using Temporal Difference Learning (Temporal Difference 학습을 이용한 다중 집단 강화.다양화 상호작용 개미 강화학습)

  • Lee Seung-Gwan
    • The Journal of the Korea Contents Association
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    • v.5 no.5
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    • pp.1-9
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    • 2005
  • In this paper, we suggest multi colony interaction ant reinforcement learning model. This method is a hybrid of multi colony interaction by elite strategy and reinforcement teaming applying Temporal Difference(TD) learning to Ant-Q loaming. Proposed model is consisted of some independent AS colonies, and interaction achieves search according to elite strategy(Intensification, Diversification strategy) between the colonies. Intensification strategy enables to select of good path to use heuristic information of other agent colony. This makes to select the high frequency of the visit of a edge by agents through positive interaction of between the colonies. Diversification strategy makes to escape selection of the high frequency of the visit of a edge by agents achieve negative interaction by search information of other agent colony. Through this strategies, we could know that proposed reinforcement loaming method converges faster to optimal solution than original ACS and Ant-Q.

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