其主要是令机械臂利用实践及结果通过神经网络算法来控制并调整自身行为从而不断趋向于完成某一目标，进而达到“学习”的目的。
As with Go, the skills required to have a robot manipulate objects, or perform other tasks, can be complex to program by hand.
通过利用合理的学习算法进行训练，神经网络对事物和环境具有很强的自学习、自适应和自组织能力。
Carries on the training through the use reasonable study algorithm, the neural network has to the thing and the environment very strong from the study, auto-adapted and from the organization ability.
只要用户输入对新结构的描述，选择算法库中一种合适的算法就能通过软件自动生成神经网络程序。
Users need only give the description of the new structure designed, then select an appropriate one in the algorithm library, and a neural network program will be generated automatically.
该方法通过神经网络技术的非线性算法，在声波曲线与自然电位、电阻率、自然伽马等多条测井曲线之间建立一种非线性关系。
The method, by means of non-linear algorithm of neural network technology, is used to set up a non-linear relation among sonic log curve, SP, Rt, Gr and curves.
本文利用前向神经网络的交叉覆盖算法，通过对文本进行分词的预处理后，实现文本的自动分类。
Based on the Crossing Cover Algorithm of forward neural network, this paper realizes the automatic classification of texts after the preprocessing of the texts.
通过计算机实验，讨论样本、学习算法和网络结构等对神经网络预测模型性能的影响及其改进措施。
Through computer simulation, samples, BP algorithms and the influence of network structure neurula on model performance have been discussed as well as the improving measures.
通过对不同情况算例的仿真，验证了神经网络目标预测算法和基于抗原进化免疫算法的正确性和有效性。
Correctness and validity of the neural network object forecast algorithm and immune algorithm with evaluated antigen are tested by the simulation of varies examples.
通过补偿模糊推理和快速学习算法的引入，使得补偿模糊神经网络在性能上优于一般的模糊神经网络。
Through the introduction of compensatory fuzzy inference and quick arithmetic, the property of compensatory fuzzy neural networks is superior to that of common fuzzy neutral networks.
本文将通过对比不同的神经网络、不同学习算法找到一种较快收敛速度及较高精度的训练方法和神经网络。
By comparing different neural networks and studding algorithms the following paragraph will discover a training method and neural network with high convergent speed and great accuracy.
通过与标准BP算法的比较，表明这两种改进方法都能有效地提高神经网络模型的精度。
By comparing with standard BP model, it shows that both the two improved methods can improve the precision of ANN efficiently.
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.
通过设计基于BP神经网络的统计加权算法，建立数据融合模型。
Through design of the statistical weighting algorithm based on BP neural network, the data fusion model will be established.
文中通过对神经网络分界面以及原神经网络算法的分析，提出一种基于球领域模型的改进的神经网络算法。
In this paper a modification approach for neural network is introduced according to the analysis of the neural network separate boundary faces and the original algorithm.
此外，通过遗传算法对模糊神经网络的学习速率和惯性系数等进行了优化，为控制系统实现最优控制提供了有力保证。
In addition, the paper makes use of Genetic Algorithms to optimize learning rates and inertia coefficients of Fuzzy-neural network, which can ensure that the controller achieves optimization control.
通过引入LM优化算法，针对矩形薄板中对称结构的损伤识别问题，提出了一种基于神经网络的分区域分步识别方法。
This paper presents an identification approach based on neural network method with sub-regions to identify damages in a rectangular plate using the LM optimized algorithm.
简述了相空间神经网络模型原理和算法，并通过实例讨论了其在水文中长期预测中的应用。
The philosophy and algorithm of a NN model of chaotic phase space are introduced, then its application for hydrology is discussed.
本文通过建立模型，以神经网络算法为基础来预测混凝土结构的抗压强度。
In this article a model is developed, based on neurocomputing, for predicting, with sufficient approximation, the compressive strength of cement conglomerates.
然后通过对真空感应冶炼工艺机理的深入分析以及对神经网络算法的研究，建立了基于RBF神经网络的终点预报模型。
Then an end-point forecast model was built based on the RBF neural network by analyzing the melting technology and the RBF training algorithm.
通过编写程序，进行数值仿真对算法的正确性进行了验证，得出神经网络算法可以用于导热反问题的求解的结论。
To verified the validity of the newly proposed algorithm, program is made and the result is prefect, so we can safely drown the conclusion that the algorithm is effective.
通过对径向基神经网络的研究，提出了径向基神经网络的构造性理论及其构造算法。
A constructive theory and its constructive algorithm of radial basis function network are presented.
由于评价人工神经网络最终学习效果是通过累积误差来进行的，从而我们直接瞄准累积误差来研究多层人工神经网络快速学习的算法。
Since we value the learning effect of neural networks by cumulative error, the paper pay direct attention to it to study the BP algorithm.
本文通过在其他领域应用广泛的神经网络BP算法，对库存的物品的可靠性进行评估。
This paper discusses how to predict the reliability of products in storage with the method of BP Algorithms which is widely used in some other fields.
最后以数值仿真得到的数据为样本数据，通过设计网络结构和选用学习算法，建立并得到基于BP人工神经网络的翘曲——收缩预测模型。
Finally, taking data from CAE as samples; the BP neural network of warping-shrinkage prediction model is established by designing the network structure and selection of learning algorithm.
本文介绍通过利用神经网络计算算法怎样将神经网络两个重要的计算特点用于自适应控制。
This paper presents how two important computional features of neural networks can be used for adaptive control through the use of neural network computional algorithms.
通过与线性插值、多项式拟合法和神经网络逼近法的比较，可以明显看出用该神经网络算法的优越性。
Compared with linear inserting value and multinomial imitation method, it is obvious that NN has more advantages.
通过离线的迭代算法生成高精度的样本点来训练神经网络，使用动量法、变学习率法和共轭梯度法提高BP网络的收敛速度。
Methods based on BP neural network and RBF neural network were studied to solve inverse kinematics. The training samples were obtained through off-line numerical method with high precision.
通过布置在不同高度处的火焰图像探测器得到循环流化床内火焰黑度沿床高的分布，进而由已经训练好的神经网络算法检测出循环流化床的床高。
Firstly, the flame emissivity were got by flame image detectors mounted at the different height of a CFB boiler, and then the bed height of the CFB boiler could be obtained by the BP neural network.
通过布置在不同高度处的火焰图像探测器得到循环流化床内火焰黑度沿床高的分布，进而由已经训练好的神经网络算法检测出循环流化床的床高。
Firstly, the flame emissivity were got by flame image detectors mounted at the different height of a CFB boiler, and then the bed height of the CFB boiler could be obtained by the BP neural network.
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