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association memory中文

發音:   用"association memory"造句
  • 聯想記憶
  • 相聯存儲器
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例句與用法

  1. A survey of the development of external and internal fantastic mental imagery association memory
    國內外奇象記憶研究發展概況
  2. Artificial neural networks ( ann ) proposes a new way for this problem because of its such advantages as parallel processing , self - adaptation , self - study , association memory , non - linear mapping , etc . thereby ann is used as fault - classifying implement in exploit ting the system
    而人工神經網絡以其分布式并行處理、自適應、自學習、聯想記憶以及非線性映射等優點,為解決這一問題開辟了新途徑。鑒于此,在開發變壓器故障診斷系統時,將神經網絡作為故障分類器進行設計。
  3. And it is obvious that pseudo - relaxation is a kind of local optimization method , so it cannot guarantee to get the global optimal solution . in this paper , a novel learning algorithm eprbam evolutionary psendo - relaxation learning algorithm for bidirectional association memory employing genetic algorithm and pseudo - relaxation method is proposed to get feasible solution of bam weight matrix
    即使在和取定后,準松弛算法的訓練和學習仍是一種局部最優化過程,它只是在初始權矩陣的附近找到第一個可行解就結束訓練,這類算法并不能保證獲得全局最優解。
  4. In order to overcome problems arisen from the application of x fluorescence analysis into complex spectrum produced by archaeological ceramic fragments with multi - element , low content and thick ground , we have employed the artificial neural network into the research of x fluorescence archaeology and conducted three kinds of research works . as the first one , we have applied the linear olam network ( optimal linear association memory network ) and the non - linear bp network ( back - propagation network ) respectively to analyze the complex x fluorescence spectrum of archaeological samples , and taken both results of spectrum analysis to compare with each other . the second , the method of pattern recognition of bp network was tentatively used to perform intelligent identification of production places of these archaeological samples
    針對科技考古中對大量考古陶片進行產地研究時x熒光分析對多元素、低含量、厚基底考古陶片產生的復雜譜分析的問題,將人工神經網絡引入x熒光考古中,進行了三方面的研究工作:一是用線性olam網絡(最優線性聯想網絡)和非線性bp網絡(誤差反傳導網絡)分別對考古樣品的x熒光復雜譜進行解譜,并比較二者的解譜效果;二是用bp網絡模式識別方法對考古樣品的產地進行智能識別;三是為了提高網絡運算的可靠性和減小基體效應及電噪聲的干擾和影響,研究并提出了三種網絡學習前的譜數據預處理方法。

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