“优质资源班班通”和“网络学习空间人人通”都是应用任务。
High quality resource classes and network learning space for everyone are the application tasks.
如何找到一种更加行之有效的RBF神经网络学习算法具有重要的理论意义和应用价值。
It has important theoretical significance and application value how to find an effective learning algorithm of RBF neural networks.
网络在我们日后的生活肯定会占据越来越重要的位置，我们应该应用在学校学习的知识和生活中的经验，积极地预防和避免上当受骗。
The network will occupy an increasingly important position in our future life, we should use the knowledge learning in school and life experience, to prevent and avoid being deceived.
遥程学习是学习者自行应用网络媒体，自动地应用和调控自己的元认知、念头和行为入行网络课程的学习。
Distance learning is that learners make use of Internet media to learn network courses by taking the initiative to use and controlling their own Metacognition, motivation and behavior.
应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性。
The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving.
实际应用表明：该模型大大提高了网络的学习效率和预测评判的精度，可以作为油气集输管道腐蚀速率预测的良好工具。
The results show that the model has a great efficiency of network learning and a high accuracy of prediction and judgement.
应用BP神经网络和GA - BP学习算法对测量方程建模，研制的悬浮物检测仪，具有较高的检测精度。
Applying the BP nerve network and GA-BP studying algorithm and carry on modeling to the equation of measuring, researching of suspended substance detector, have the higher examination accuracy.
动态贝叶斯网络(DBN)，以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
支持向量机是继神经网络后机器学习的热点研究技术，它主要应用于分类和回归问题中。
SVM is the hot issue accompanying artificial neural network in machine learning. It involves any practical problems such as classification and regression estimation.
基于多媒体网络可以提供特殊的资源和环境支持，设计了一种新的应用于大学物理的学习方法。
As the multimedia network can provide special resources and environment, the article designs a new approach in college physics study.
随着计算机和网络在教育教学中广泛深入地应用，数字化学习资源开始影响我们的学习和生活。
With the wide and deep application of computers and Internet in teaching and learning, Digital learning Resources is influencing our study and life.
本文通过案例研究探讨了一种基于网络的协作学习和应用模式。
This paper studies a practice module of the based-web CL by cases studying.
实际应用表明，该模型将大大提高网络的学习效率和预测评判的准确率。
The application of the optimized model to the pipeline corrosion rate prediction shows that it will prove greatly the learning effectively and the accuracy in prediction and judging.
而人工神经网络以其自适应、并行性、非线性、鲁棒性和学习特性被广泛应用于语音识别领域。
ANN is widely used in speech recognition field due to its self adapting, parallelism, non-linearity, robustness and learning ability.
同时，应用神经网络的学习和记忆功能，对控制变量的隶属函数和控制规则进行优化，使控制方案更趋于合理。
At the same time, using the study and memory ability of neural networks, optimize subject function and control rules of control variables, which makes the control scheme more reasonable.
本文以英语教学的自身特点和网络环境下的学习模式为基础，简要论述了可以应用在以网络为基础的大学英语教学方面的一些评价策略。
This paper briefly discusses some evaluation strategies for application of web to college English teaching based on the characteristics of English teaching and the learning mode in web context.
采用一种基于免疫算法和最小二乘法的两级学习方法设计径向基函数（RBF）网络，并将其应用于雷达天线扫描方式识别系统。
A hybrid RBF training method based on immune algorithm and least square method is proposed and applied in radar antenna scanning-style recognition system.
应用一种变结构神经网络算法对初始化的模糊规则进行调整，提高模糊控制规则的自学习和自适应能力。
A kind of variable structure neural network algorithm is adopted to adjust fuzzy rules, and improves the ability of self-studying and self-adjusting in fuzzy control rules.
和RBF神经网络技术以其强大的学习功能应用于水资源分类，取得了很好的效果。
Ack propagation and radial basis function neural network methods have been applied to water resources areas due to theirs powerful learning abilitys and many good results have been achieved.
RBF神经网络采用离线学习在线修正权值和阈值，为加快收敛速度，应用带惯性项的梯度下降法。
RBF neural network adopts the off-line training and the on-line adaptation of weight and threshold value. In order to speed up the convergence, the grads descent method with inertia item was used.
本文介绍了动态对角递归网络，并针对BP算法收敛慢的缺点，将递推预报误差学习算法应用到神经网络权值和域值的训练。
To overcome the slow convergence of the BP algorithm, recursive prediction error algorithm is proposed, which can train both the weight and the bias.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
提出了自适应学习率及动量因子的BP神经网络算法和误差逼近度渐近收缩学习的BP神经网络算法，并将其应用于汽轮发电机组振动故障诊断与识别。
The improved BP algorithms based on adaptive parameters adjustment and error contracting gradually are presented, which are applied successfully to fault diagnosis of steam- turbine generator unit.
由于神经网络技术具有很强的自适应性、学习性和容错性，因此被广泛应用于各个领域。
Neural network is used widely in many fields as it' s strong self-adapting, self-learning and error tolerance abilities, with which image recognition is implemented.
运用学习的原理和规则设计、开发、实现计算机、多媒体、超媒体和网络应用等的教学材料和项目。
Apply rules and principles of learning to designing, developing, and implementing instructional materials and programs including computer, multimedia, hypermedia, and Web applications.
基于模糊熵准则和误差平方和准则建立了模糊学习算法，基于该模糊学习算法，应用BP神经网络对柜式空调机组的性能进行了模拟。
Based on fuzzy entropy rule and sum-squared error rule, a fuzzy learning method was presented. According to this method, BP neural network was used to model the performance of air-conditioning units.
基于神经网络的交通流预测模型已被广泛应用于ITS由于其较高的预测精度和自我学习能力。
The traffic flow forecasting model based on neural network has been applied widely in its because of its high forecasting accuracy and self-learning ability.
开发者现在不得不学习新的体系结构原则、新的网络通信技巧和新的应用部署方式。
Developers are having to learn new architectural principles, new communications techniques, and new ways of deploying functionality.
应用神经网络理论，本文提出了圆弧破坏和楔体破坏的边坡安全系数估计的新方法，为解决安全系数估计的知识的学习问题，提出了一种推广学习算法。
With application of neural network theory, a new method has been proposed for direct estimation of the safety factor for circular and wedge failure of slopes.
讨论了薄膜厚度控制的非线性和耦合性，应用神经网络串行解耦算法，解决多变量非线性耦合问题，同时还采用了改进的学习算法———动量法，并与传统算法做了仿真比较。
Discusses the nonelinearity and coupling of the plastics thickness control system, and adopts NN serial decoupling algorithm to solve the difficulty in multivariable nonlinear coupling.
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