연구동향 분석

양방향 LSTM기반 시계열 특허 동향 예측 연구

최승환 김광수 곽수영 2022년

논문상세정보
인용/피인용
' 양방향 LSTM기반 시계열 특허 동향 예측 연구' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Bidirectional LSTM Neural Network
  • Deep learning
  • Forecasting emerging technology
  • Patent analysis
  • bass model
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 양방향 LSTM기반 시계열 특허 동향 예측 연구' 의 참고문헌

  • 특허정보를 활용한 기술 확산 예측: NCW 정보보호기술을 중심으로
    김도회 [2009]
  • 특허 토픽 모델링과 성장주기곡선을 통한 유망기술 발굴
    김갑조 [2017]
  • 토픽모델 및 특허분석을 통한 차량용 반도체 기술 추세 분석
    남대경 [2018]
  • Understanding LSTM Networks
  • Predicting future technological convergence patterns based on machine learning using link prediction
  • Patent indicators for the technology life cycle development
    R. Haupt [2007]
  • Novel mixed-encoding for forecasting patent grant duration
    Raman Dutt [2021]
  • NLS와 OLS의 하이브리드 방법에 의한 Bass 확산모형의 모수추정
    홍정식 [2011]
  • Methods for forecasting numbers of patent applications at the European Patent Office
  • Long short-term memory recurrent neural network architectures for large scale acoustic modeling
    H. Sak [2014]
  • Long Short-Term Memory
  • Learning long-term dependencies with gradient descent is difficult
    Y. Bengio [1994]
  • Fundamentals of recurrent neural network ( RNN ) and long short-term memory ( LSTM ) network
  • Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis
  • Forecasting emerging technologies: A supervised learning approach through patent analysis
  • Exploratory research on the analysis of national R&D programs using growth model
    D. Lee [2014]
  • Dynamic recurrent neural networks: Theory and applications
  • Bidirectional recurrent neural networks
    M. Schuster [1997]
  • A novel approach to forecast promising technology through patent analysis
    Gabjo Kim [2017]