在神经网络训练的梯度表示的价值是什么？
What does the value of the gradient during NN training signify?
神经网络训练过程中的高昂计算代价是有待克服的一个主要困难。
The high computational cost in the training process of neural networks is a major inconvenience.
目前已广泛应用于函数优化，神经网络训练，模糊系统控制等领域。
Has been widely used in function optimization, training neural networks, fuzzy systems control, and other fields.
在神经网络训练的基础上，采用遗传算法优化神经网络的输入参数。
Based the successfully trained ANN model, genetic algorithms (GA) are used to optimize the input parameters of the model.
此外，它并不需要耗费的时间的训练过程中脱机并保存的神经网络训练的时间。
In addition, it does not require the time-consuming training process offline and saves the time of neural network training.
在研究蚁群优化神经网络训练算法的基础上，建立了矿井提升机减速器齿轮故障诊断模型。
On the basis of researching the optimization of neural network through ant colony algorithm, the mine hoist reducer gear failure diagnosis model is established.
提出了一种适于雷达在线预测的新的前向神经网络训练的优化步长初值选择方法及调整方法。
The main results can be summarized as following:(1) A novel method of selecting the initial optimum step and its adjustment is presented in FNN, which is suitable for the prediction on-line.
BP算法是前馈神经网络训练中应用最多的算法，但其具有收敛慢和陷入局部极值的严重缺点。
BP algorithm is the most popular training algorithm for feed forward neural network learning. But falling into local minimum and slow convergence are its drawbacks.
基于梯度下降的神经网络训练算法易于陷入局部最小，从而使网络不能对输入模式进行准确分类。
Neural network BP training algorithm based on gradient descend technique may lead to entrapment in local optimum so that the network inaccurately classifies input patterns.
通过高精度的数控移动工件台获取密集的样本数据，并在神经网络训练过程中采用贝叶斯正则化方法。
Dense sample data are acquired by using numerical control platform of high precision, and the Bayesian generalization is adopted during training the neural network.
通过对BP算法和RBF算法的研究，确定了适合环境监测数据COD值的神经网络训练与预测模型。
Based on the research of the BP arithmetic and RBF arithmetic, the NN training and forecast model which is suitable for the environment-monitoring data is proposed.
目前微粒群算法已广泛应用于函数优化、神经网络训练、数据挖掘、模糊系统控制以及其他的应用领域。
Recently, Particle Swarm optimization is applied into function optimization, Neural Networks, data mining, Fuzzy Control System and other application field.
目前这类算法已被广泛应用于机器学习，人工智能，自适应控制，人工神经网络训练，图像处理等各个方面。
This algorithms has been widely used in machine learning, artificial intelligence, adaptive control, artificial neural network training, Image processing, among other areas.
本文使用RBF神经网络和改进的BP神经网络进行神经网络训练，其分类结果都达到了较好的效果和精度。
In this paper, RBF neural network and improved BP neural network are used for training. The results have both reached good effect and accuracy.
t检验结果表明，BP神经网络训练和预测得到的模拟值与实测值之间吻合很好，该方法具有较高的预测精度。
The t-test result showed that the prediction value agreed with the observation data fairly well and the BP neural network model had rather high prediction precision.
该系统能够完成罐车图像的预处理、PCA特征提取、BP神经网络训练以及基于BP神经网络的识别等功能。
This system can complete the function of picture pretreatment, PCA characteristic pick-up and BP nerve network training as well as recognize and so on.
通过对模糊神经网络训练，建立干扰和半导体生产线状态等输入参数与优化的重调度策略输出之间的映射关系。
The relation between the input of FNN, such as disturbance, system state parameters, and output of FNN, optimal rescheduling strategy, is built by FNN.
该系统主要由以下模块构成：用户界面模块、网络模型模块、样本处理模块、神经网络计算模块以及神经网络训练结果显示模块。
The system consists of graphic user's interface module, sample processing module, neural network structural module and neural network computing module.
针对传统BP神经网络训练中收敛速度较慢的缺点，提出一种基于L - M算法的神经网络应用于机械设备故障诊断的专家系统。
An improved neural network based on L-M algorithm has been applied to fault diagnosis expert system against to the slow convergence rate of conventional BP neural network.
本文在使用BP神经网络对自相关过程进行监控的基础之上，对隐层神经元数对于神经网络训练收敛性及识别率的影响进行分析研究。
In this research, various number of hidden nodes of neural network is studied to improve the training result and identification capability of BP neural network.
在文中的第五章，对补偿模糊神经网络训练的规则用经过噪声污染的数据进行验证，结果表明，网络能比较真实的反应理论结果的变化趋势。
In the chapter 5 the trained fuzzy rule is confirmed by the data adding random noise, the result shows that compensatory fuzzy neural network can respond the trend of theory variety.
将人工神经网络理论和算法应用于双辉离子渗碳的研究，在对人工神经网络训练的基础上，建立了双辉离子渗碳工艺与渗层性能预报的数学模型。
On the basis of the training of network, the mathematics model on the relationship between double glow plasma carbonizing and prediction of properties is built.
将已经通过工程手段计算出结果的数据以输入单元、隐含层和输出单元的形式代入系统进行神经网络训练，不同的屈服准则对应训练出不同的网络系统。
Take known data to the system to train the networks. According to different strength criterions, the different networks were trained. After that, the networks with new data were checked up.
监督学习是训练神经网络和决策树的最常见技术。
Supervised learning is the most common technique for training neural networks and decision trees.
McGuire的下一个计划是通过训练神经网络使之能够识别不同的纹理。
McGuire next plans to train the network to process different textures.
有意思的是，神经网络是一种对学习型评估的进化，算法经过训练后其行为就像是一个人类专家。
It is interesting to note that Neural Networks is an evolution of learning-oriented estimation, in which the method algorithm is trained to behave like a human expert.
有意思的是，神经网络是一种对学习型评估的进化，算法经过训练后其行为就像是一个人类专家。
It is interesting to note that Neural Networks is an evolution of learning-oriented estimation, in which the method algorithm is trained to behave like a human expert.
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