Connectionist fuzzy classifier for speech recognition

Yau Hwang Kuo, Cheng I. Kao, Jiahn Jung Chen

研究成果: Article同行評審

摘要

A neural network model based on the fuzzy classification concept, called the Connectionist Fuzzy Classifier (CFC), is proposed. The CFC model originates from embedding a 'weighted Euclidean distance' fuzzy classification procedure into a four-layered neural network architecture. It employs a one-pass learning algorithm, which can overcome the two major drawbacks of the backpropagation model: the local minimum problem and long training time. Some experiments and comparisons between CFC and some different neural network models are made in this paper. The experimental results show that the CFC model has better accuracy for speech recognition than do the PNN, backpropagation, and linear matching methods, especially in a noisy environment.

原文English
頁(從 - 到)1-20
頁數20
期刊Journal of Information Science and Engineering
10
發行號1
出版狀態Published - 1994 三月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 硬體和架構
  • 圖書館與資訊科學
  • 計算機理論與數學

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