頁籤選單縮合
題 名 | A Hierarchical Neural Network for Hidden Markov Models |
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作 者 | 周文光; | 書刊名 | Journal of Information Science and Engineering |
卷 期 | 13:2 1997.06[民86.06] |
頁 次 | 頁207-234 |
專 輯 | Special Issue on Neural Networks |
分類號 | 310.15 |
關鍵詞 | Neural networks; CTMAXNET; Hidden markov models; Viterbi algorithm; Hierarchical neural model (L[feb0]); Baum-welch reestimation algorithm; |
語 文 | 英文(English) |
英文摘要 | TheHidden Markov Model(HMM) has been widely and successfully used in speech recognition. However, it is difficult to design an HMM that operates in real time, as is required for automatic speech recognition or automatic target recognition. Instead of a conventional sequential computation environment, the use of a massively parallel computing environment for implementing an HMM should be explored. In this paper, a hierarchical neural model called theHidden Markov Learning Machine(HMLM) is proposed that successfully solves all three key problems concerning HMMs,learning, recalling, anddecoding. Because it provides a constant time response in the recalling phase, the HMLM provides the potential for real-time processing. The HMLM is constructed based upon the hierarchical neural model L3 [9],CTMAXNET, CTMINNET, labeling networks [8], and a special learning heuristics controller, REPLA [10]. An analysis of the time complexity of HMLM is also presented. The significance of this research lies in the progress from a stochastic model to a solid mechanism, similar to the progress from PROLOG to PROLOG machines. |
本系統中英文摘要資訊取自各篇刊載內容。