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激活函数的英文

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"激活函数"怎么读用"激活函数"造句

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  • activation function

例句与用法

  • Importance of optimizing neural activation function types
    优化神经元激活函数类型的重要性
  • And that the conclusion can also be generalized to multi - channel system . in addition , some practical considerations , e . g . ,
    3 .参数自适应fastica算法非线性激活函数( nonlinearactivationfunction )和独立性约束密切相关,是ica估计的关键因素之一。
  • Based on the analysis of all kinds of spaces in the surgical environment of orthopaedic clinic , the set with constraint - activated functions is adopted to describe clinical spaces
    摘要在分析骨科临床手术环境中各类空间的基础上,用带有约束激活函数的集合对临床空间进行了描述。
  • First we introduce its basic idea , typical activating functions , learning rules and its main application : classification & c1ustering , associated memory and optimiza tion
    我们先简要介绍了人工神经元网络的基本思想,典型激活函数,学习规则和主要应用:分类聚类,联想记忆和优化。
  • The dissertation is dedicatd to the theory of maximum nongaussianity estimation in ica , and some problems are emphasized , e . g . , the uniqueness of estimation , the convergence of fastica , the constraint of independence and the nonlinear activation function , the algorithms for multiple components and the order of independnent components
    本文重点研究最大非高斯估计的相关理论问题,包括估计的唯一性,算法的收敛性,独立性约束分析和非线性激活函数,独立分量的排序和子空间的选择,具有时间结构信息的信号源的估计等。
  • Our condition and estimate are formulated in terms of the network parameters , the neurons ’ activation functions and the associated equilibrium point . hence , they are easily checkable . it is believed that these results are significant and useful for the design and applications of the delayed hopfield neural networks
    这些条件和估计的公式是由网络参数、神经元激活函数以及相应的平衡点构成,所以它们很容易使用,相信这些结果对于带时间延迟的hopfield神经网络的设计和应用具有一定的重要性和使用价值。
  • Hi the aspect of symmetry analyzing to the hopfield model neural network with hebbian learning , we study on the dynamical behavior of the state space under the action of isometric transformation group g = z2 ? n , and prove the invariant property of the energy orientation ? / / " ) of the state space under the action of g . we find that the symmetry relationship of the network is sx - sw = sh when the active function of the neuron is odd , where sx is the symmetry of the patterns set x under hebbian learning rule , sh is the symmetry of the network and sw is the symmetry of the weight matrix w of the network
    ) s _ n为手段,研究了网络状态空间在群g作用下各点的运动情况,证明了群g作用下的不变性。证明了当神经元的激活函数f为奇函数时, hebb法则下存储样本集x的对称性s _ x 、网络对称性s _ h以及连接矩阵对称性s _ w三者之间满足s _ x = s _ w = s _ h的关系;同时,我们还证明了:网络稳定态集vf同一s _ h轨道中的两个稳定态的动力学行为(能量和吸引域大小)相同;两个等距网络h和h 1 = g ? h , ( ? ) g (
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