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- W259471034 abstract "본 논문에서는 모호성을 포함하고 있는 시계열 패턴인식을 위한 새로운 인식모델인 순환퍼지기억장치를 제안한다. 순환퍼지기억장치는 기존의 퍼지기억장치에 순차적인 입력패턴를 처리하고 시간적 관련성을 표현할 수 있는 순환층을 추가함으로써 확장된 모델이다. 본 논문에서 제안하는 순환퍼지기억장치는 입력과 출력사이의 관련정도를 설정하기 위해 헤비안 방식의 학습알고리즘을 사용한다. 그리고 순환퍼지기억장치의 순환층에 필요한 가중치를 학습하기 위해서 오류역전파 알고리즘을 이용한다. 본 논문에서는 제안하는 모델을 음성신호의 경계를 추출하는 문제에 적용하여 성능을 평가한다. This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal." @default.
- W259471034 created "2016-06-24" @default.
- W259471034 date "2004-08-01" @default.
- W259471034 modified "2023-09-25" @default.
- W259471034 title "A Novel Model, Recurrent Fuzzy Associative Memory, for Recognizing Time-Series Patterns Contained Ambiguity and Its Application" @default.
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- W259471034 doi "https://doi.org/10.3745/kipstb.2004.11b.4.449" @default.
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