descent method

descent method的基本解释

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[计] 下降法

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

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

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。

Main points of the thesis are as follows:(1) The main function models and information relations between these models in the CAPP system in the integrated CAD/CAM project are explained, a new process plan design method is mentioned based on analyses of process plan design. This method is based on the process plan prototype, and develops the prototype little by little until the perfect process plan is completed, the prospect of this method is discussed;(2) The feature abstracting rules and methods are studied, a feature coding system is developed based on GT, all the defined feature models are described by the mathematical methods;(3) The integrated method of CAD/CAPP based on features is mentioned, this method defines a feature exchanging model, and develops an interface software to exchange design data into technology data base, this technology data base includes the design and technology information of the parts;(4) The process plan reasoning method based on features is created, this method uses Mycin algorithm to solve the reasoning of the feature process plan. A process plan making system is developed. In this system part process plan is made by the semi-intelligent method, this method uses the feature reasoning, feature process chain searching and man-computer talking together;(5) The main structure of the HOPE system is introduced, a shaft drawing and its process plan made by this system are shown, the data process problem in process drawing making is also discussed;(6) A new method of describing process rule is introduced, this method uses IfThen rules and neural nets weight values together to describe process rules, the BP algorithm is adopted in this method;(7) The optimal machining sequence problem of the process steps on the same fixturing is discussed, optimal algorithms SA is applied to solve this problem, the result shows that this method is more efficient than the traditional method;(8) All jobs in the thesis are summarized. In order to carry out the research in the future, some rational proposals are given.

全文论述的内容主要有下列几个方面：(1)较为系统地阐述了CAD／CAM一体化工程项目中CAPP系统的主要功能模块及其信息流程，在分析工艺设计过程的基础上，提出了基于特征原型的渐进式工艺设计方法，并探讨了该方法的应用前景；(2)研究了零件特征提取的原则和方法，在成组技术的基础上，开发了一套基于特征的分类编码系统，并在提取零件特征的基础上，定义了零件特征的数据模型，开发了基于轴类零件特征的参数化设计系统；(3)研究了基于特征的CAD／CAPP集成的方法，通过定义零件特征数据模型的存储结构，开发了将设计数据转换到工艺数据库中的接口程序，为后续的CAPP系统提供必要的零件形状和工艺信息；(4)研究了基于特征的工艺推理方法，将Mycin算法应用到工艺决策中，开发了基于特征的工艺推理及工艺方案生成系统，在系统中综合运用特征推理、特征加工工艺链查询及人机对话相结合的半智能化方法确定零件加工工艺；(5)介绍了整个HOPE系统的总体结构，通过运行HOPE系统绘出了一轴类零件的零件图，并生成了该零件的加工工艺方案，初步探讨了基于特征的工序图生成技术中数据处理问题；(6)研究了工艺设计过程中知识如何有效表示的问题，提出了将显式的IF-THEN规则表示与隐式神经网络权值表示相结合的工艺知识表示方法，并利用BP算法进行了工艺知识表示的初步研究；(7)研究了工艺设计过程中同一安装下工步优化排序问题，利用神经网络中基于模拟退火的理论来解决该工步优化排序问题，实践证明该方法较传统的算法具有更高的效率；(8)总结了本文所进行的研究工作，为今后进一步开展工作提出了较为合理的建议。

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

descent method：下降法

descent 下降 | descent method 下降法 | description 描述

steepest descent method：最速下降法

最速下降法(steepest descent method)由法国数学家Cauchy于1847年首先提出. 在每次迭代中,沿最速下降方向(负梯度方向)进行搜索,每步沿负梯度方向取最优步长,因此这种方法称为最优梯度法. 最速下降法是一种最基本的算法,

steepest descent method：最陡下降法

整个模拟过程按如下步骤进行:运用最陡下降法(steepest descent method)对多肽进行2000步优化计算,以避免做分子动力学时原子间的不合理碰撞;在多肽周围加上1 nm厚度的水层,保证其有足够构象变化空间;对溶液状态下的多肽设定pH=7;

steepest descent method：最陡坡度法

Steepest decent algorithm 最陡下降演算法 | Steepest-descent method 最陡坡度法 | Stein estimator 史坦估計量

steepest descent method：最陡下降法; 最速下降法

stationary point 驻点 | steepest descent method 最陡下降法; 最速下降法 | Stochastic Optimization:随即最优化