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differentiable相关的网络例句

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与 differentiable 相关的网络例句 [注:此内容来源于网络,仅供参考]

Through above study, the theoretical analysis of the inherent fault tolerance of feed-forward neural networks has been extended from hard-limit activation functions to differentiable activation functions; 2. By using above method, fault tolerance of discrete Hopfield feedback neural networks have been also analyzed. It means that the above method can be applied not only for feed-forward neural networks but also for feedback neural networks; 3. For the feed-forward neural network with the Sigmoid activation function, Chebyshev Inequality method was presented.

这项研究将以前仅限于对硬限幅作用函数前向神经网络固有容错性能的理论分析推广到具有可微作用函数的前向神经网络; 2 针对离散型Hopfield反馈神经网络,利用上面提出的方法,对其同样进行了容错性分析,得出许多有用的结论和计算公式,说明上面提出的方法不仅适用于前向神经网络,同时也适用于反馈神经网络; 3 针对具有Sigmoid作用函数的前向神经网络提出了一种切比雪夫不等式法。

If is continuous on the closed interval and differentiable on the open interval, then there is at least one number in where

在闭区间上连续;(2)在开区间内可导

This paper discusses a variation equation problem in a class of singular stochastic control with stopping time,gives its solution under two different conditions,which is a one order continuous differentiable and concave function,and gives the exact form.

讨论了一类带停时的奇异型随机控制问题中的一个变分方程问题,并且在两种不同的情况下给出了该变分方程的解,即为一阶连续可导凹函数,并在两种情况下给出了此函数的具体形式。

Starting from the concept of conjugate analytic function,this paper expounds on the necessary and sufficient conditions of conjugate analytic of differentiable complex function represented in exponent form.

在共轭解析函数定义基础上,讨论指数形式表示的函数共轭解析的充要条件

The function is continuously differentiable.

函数是连续可微分的。

X, s. t. g≤0(i=1, 2,…, m), where x∈R and ? g: R→R are twice continuously differentiable convex function.

x,s.t.g≤0(i=1, 2,…, m),其中,x∈R;f,g:R→R为二次连续可微凸函数。

Consider the convex programming problems min, s. t. g≤0(i=1, 2,…, m), where x∈R and g: R→R are twice continuously differentiable convex function.

对于凸规划问题min,s.t.g≤0(i=1, 2,…, m),其中,x∈R;f,g:R→R为二次连续可微凸函数。

These results include several sufficient conditions for the global exponential stability of general neural networks with time-varying delays and without monotone, bounded, or continuously differentiable activation function.

不同于已有文献用Liapunov函数或泛函法研究稳定性问题时,将时滞项作为干扰处理的方法,给出了利用时滞项让系统稳定的结果。

Firstly, we prove that if we select nodes properly, and let the distance between these nodes sufficiently small, cubic polynomial spline can approximate a continuously differentiable function, which with continuous derivatives of second order and discontinuity of the first kind in the derivatives of fourth order, to any desirable accuracy. This result lays a theoretical foundation for representing trajectories by splines and double orthogonal basis.

一是证明了适当选取节点并使节点距充分小时,三次多项式样条可以逼近具有二次连续导数的分段四次连续可微函数到足够的精度,这一结论为用样条和双正交基表示弹道打下了理论基础。

In this paper, we present a nonmonotone line search model algorithm for the nonsmooth composite minimization problems minhf(x, where each f: Rn→Rn is a locally Lipschitzian function, and h: Rn→R is a continuously differentiable convex function.

1引言考虑下面的复合极小化问题minF=hf(x,(1 。1)二〔丫其中f:R"一R"是局部Lipschitz函数,h:Rn一R是下有界的连续可微的凸函数。对于问题(l 。1),QIL、袁亚湘和孙文瑜等z1一

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