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descent method相关的网络例句

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

General method, including steepest descent method, conjugate gradient method and Dynamic Programming, is to search piecewise functions as the approximation with numerical methods on the base of Bellman\'s principle of optimality or the Hamiltonian function.

动态规划与智能仿生算法尤其适用于梯度信息不可得的情况。单一的优化方法用于不同情况下的化工动态优化问题未必可行有效,为此本文针对具体情况,提出了多种解法的集成策略。

The characteristics of the steepest descent method's small amount of computations and conjugate direction method's fast convergence combined, a conjugate vector base method for solving ill-conditioned linear equations was proposed.

结合最速下降法计算量小和共轭方向法收敛速度快的特点,提出了一种求解病态方程组的共轭向量基的方法。

General method, including steepest descent method, conjugate gradient method and Dynamic Programming, is t...

新型的智能仿生算法在动态优化问题中的应用也逐渐增多。

Two ways are used to design the network,the one is the direct energy descent method,and the other is the gradient descent method .

有两种方法可以用来设计网络,一是能量下降法,另一个是梯度下降法。

In the meantime, it can be seen that the conjugate gradient method or steepest descent method is a special case of the mixed method.

同时也可以看到共轭梯度法与最速下降法仅仅是混合算法的特例。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一,然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点,因此,本研究提出一个梯度预测搜寻法则(gradient forecasting search method, GFSM)来改善传统梯度法的缺点,用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性;而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素,传统统计为基础之预测方法的缺点是需要较大量的数据进行预测,因此计算复杂度高,灰色预测模型具有建模资料少且计算复杂度低等优点,然而灰色预测理论以连续之微分方程式为基础,并且透过一些数学上的假设与近似,将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测,这样的作法不尽合理,且缺乏数学理论上的完备性,因为在转换过程中已经造成建模上的误差,且建模过程仅考虑相邻的两个资料点关系,无法正确反应数列未来的变化趋势。

The Weil Descent's algebraic method for DLP in the Jacobian of hyperelliptic curves which generalized by Galbraith is analyzed, and the problem of whether or not the Weil Descent'method can be used to attack the HCDLP with the form of y〓+xy=f defined over GF is discussed in detail.

研究了Galbraith提出的对超椭圆曲线的离散对数问题的WeilDescent代数攻击方法,对定义在GF上的形如y〓+xy=f的HCDLP能否用Weil Descent代数方法攻击作了详细讨论。

We get the following conclusions: the Weil Descent's algebraic attack method is not a threat to hyperelliptic curves with the form of y〓+xy=f defined over GF (2〓) when r is large.

得出结论:Weil Descent代数攻击对这类超椭圆曲线所建立的密码体制并未造成太大的威胁。

A mixed method of conjugate gradient method and steepest descent method ;2. The Modified Steepest Descent Method──Best Point in Steepest Descent method;3. This paper presents the mathematical model for the optimization of heterogeneous components, and the method using sensitivity analysis and steepest descent method to optimize material properties, the component is then identified.

阐述了非均质材料零件设计优化的数学模型,并采用灵敏度分析以及最速下降法对其各个材料区域的材料性能进行设计优化,得到最佳材料性能参数后,再从非均质材料数据库中找到相应的工程材料,合成满足设计要求的非均质材料零件该方法为设计者提供了切实可行的非均质材料零件的材料设计方

The mixed method raise the convergent rate of the conjugate gradient method, and solve the problem which the steepest descent method can not solve in the condition with badly characteristics for the objective function.

混合算法既提高了共轭梯度算法的收敛速度,又解决了目标函数"性态不优"时,最速下降法难以求解的问题。

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