重建曲线至少有6个控制点。这是必要的，以实现理想的连续性当使用曲线表面从网络或扫描第2轨。
Rebuild the curve with at least 6 control points. This is necessary to achieve the desired continuity when using surface from curve network or sweep 2 rails.
针对信息科学和控制理论中经常涉及的一类泛函极值问题，提出基于连续回归神经网络的求解方法。
In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control.
介绍了以连续合格品数为控制对象的方法以及实现该方法的神经网络核心技术。
The method of taking continuous qualified product counts as the control objects and the kernel technology of neural network for realizing this method are introduced.
针对连续空间下的强化学习控制问题，提出了一种基于自组织模糊rbf网络的Q学习方法。
For reinforcement learning control in continuous Spaces, a Q-learning method based on a self-organizing fuzzy RBF (radial basis function) network is proposed.
控制系统网络为工业以太网结构，能够长期连续运行。
The control system network is based on industrial Ethernet structure and is capable of long-term continuous operation.
前馈神经网络由于具有理论上逼近任意非线性连续映射的能力，因而非常适合于非线性系统建模及构成自适应控制。
Because the feedforward neural network has an ability of approach to arbitrary nonlinear mapping, it can be used effectively in the modeling and controlling of nonlinear system.
因此尝试采用模糊神经网络控制技术来进一步提高铜连续挤压生产的自动控制水平。
So we attempt to improve furtherly auto-control level of copper continuous extrusion production by fuzzy neural network control technology.
通过数字复合正交神经网络的连续化算法处理获得了一种模拟复合正交神经网络，并作为前馈控制器。
The analog compound orthogonal neural network was obtained by means of a continuous algorithm treatment for a digital compound orthogonal neural network, and was used as the feedforward controller.
因此尝试采用BP神经网络控制技术来进一步提高铜连续挤压生产的自动控制水平。
So we have attempted to apply the BP Neural Network Technology to improve further the automatic-control ability of the continuous extrusion process of copper.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
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