association memory meaning in English
联想记忆
相联存储器
Examples
- A survey of the development of external and internal fantastic mental imagery association memory
国内外奇象记忆研究发展概况 - 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
而人工神经网络以其分布式并行处理、自适应、自学习、联想记忆以及非线性映射等优点，为解决这一问题开辟了新途径。鉴于此，在开发变压器故障诊断系统时，将神经网络作为故障分类器进行设计。 - 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，准松弛算法的训练和学习仍是一种局部最优化过程，它只是在初始权矩阵的附近找到第一个可行解就结束训练，这类算法并不能保证获得全局最优解。 - 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
针对科技考古中对大量考古陶片进行产地研究时x荧光分析对多元素、低含量、厚基底考古陶片产生的复杂谱分析的问题，将人工神经网络引入x荧光考古中，进行了三方面的研究工作：一是用线性olam网络(最优线性联想网络)和非线性bp网络(误差反传导网络)分别对考古样品的x荧光复杂谱进行解谱，并比较二者的解谱效果；二是用bp网络模式识别方法对考古样品的产地进行智能识别；三是为了提高网络运算的可靠性和减小基体效应及电噪声的干扰和影响，研究并提出了三种网络学习前的谱数据预处理方法。