1) Inv-AprioriAll Algorithm
逆-AprioriAll算法
2) AprioriAll algorithm
AprioriAll算法
1.
This paper expounds principles of path pattern mining in details,then according to the characteristics of the mining object,improves the function which generating candidate sequences in AprioriAll algorithm.
对路径模式挖掘的原理做了详细的阐述,并且针对挖掘对象的特性,对AprioriAll算法中生成侯选序列的函数做了相应的改进。
3) quasi-inverse algorithm
准逆算法
4) inverse approach
逆算法
1.
The FE inverse approach and the optimization of process parameters in sheet metal forming;
有限元逆算法及其在板料成形工艺优化中的应用
2.
Based on inverse approach of FE and by sensitivity analysis optimization BFGS(Broyden-Fletcher-Goldfarb-Shanno) method,considering FLC and WLC curve as object function,this paper presented a method to optimize the drawbead restraining forces and position in sheet metal forming process for optimism of drawbead sensitivity analysis.
基于有限元逆算法和灵敏度优化的BFGS(Broyden-Fletcher-Goldfarb-Shanno)算法,提出一种以成形极限曲线和起皱极限曲线作为目标函数,优化拉深成形中的拉深筋位置和大小的拉深筋灵敏度优化算法。
3.
Based on ideal deformation theory, the authors developed a finite element inverse approach for sheet metal forming process simulation, and the computer program was implemented.
依据理想形变理论 ,研究开发了冲压成形过程模拟的有限元逆算法 ,并考虑了成形中的压边力 ,拉延筋等工艺条件 ,实现了计算机程序。
5) backstepping algorithm
逆推算法
1.
A simplified nonlinear robust control algorithm was presented so as to improve the robustness of the designed controller for a nonlinear course-keeping system for ships through simplifying backstepping algorithm,which connected the backstepping algorithm with the closed-loop gain shaping algorithm.
为提高控制器的鲁棒性能,针对非线性船舶航向保持系统,将简化的逆推算法与闭环增益成形算法相结合,设计出非线性鲁棒控制器。
2.
For Norrbin ship mathematical model with nonlinear and uncertain characteristics,the ship steering robust controller was designed successfully based on backstepping algorithm and nonlinear damping algorithm.
针对带有不确定项的Norrbin非线性船舶模型,将逆推算法和非线性阻尼算法相结合,设计非线性船舶航向鲁棒控制器。
6) reversible algorithm
可逆算法
补充资料：逆推学习算法
分子式：
CAS号：
性质：又称逆推学习算法，简称BP算法，是1986年鲁梅哈特(D. E. Rumelhart)和麦克莱朗德(J. L. McClelland)提出来的。用样本数据训练人工神经网络(一种模仿人脑的信息处理系统)，它自动地将实际输出值和期望值进行比较，得到误差信号，再根据误差信号从后(输出层)向前(输入层)逐层反传，调节各神经层神经元之间的连接权重，直至误差减至满足要求为止。反向传播算法的主要特征是中间层能对输出层反传过来的误差进行学习。这种算法不能保证训练期间实现全局误差最小，但可以实现局部误差最小。BP算法在图像处理、语音处理、优化等领域得到应用。
CAS号：
性质：又称逆推学习算法，简称BP算法，是1986年鲁梅哈特(D. E. Rumelhart)和麦克莱朗德(J. L. McClelland)提出来的。用样本数据训练人工神经网络(一种模仿人脑的信息处理系统)，它自动地将实际输出值和期望值进行比较，得到误差信号，再根据误差信号从后(输出层)向前(输入层)逐层反传，调节各神经层神经元之间的连接权重，直至误差减至满足要求为止。反向传播算法的主要特征是中间层能对输出层反传过来的误差进行学习。这种算法不能保证训练期间实现全局误差最小，但可以实现局部误差最小。BP算法在图像处理、语音处理、优化等领域得到应用。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条