对于那些网络速度慢的用户来说，下载时间会很长。
Plus the load time can be far too long for those without high-speed Internet connections.
如果网络速度慢、网络流量大或者Web页面较大，这就显得更加重要了。
This becomes even more important when network speed is low, network traffic is high, or the size of the Web pages is large.
移动计算设备如pda、手机等具有资源少、网络速度慢的特点，因而其接收电子邮件的速度较慢。
Mobile computing devices such as PDA, mobile phone are of limited resource and slow Internet access capability, so the speed of receiving email is relatively slow.
该技术的缺点是网络带宽使用效率低、速度慢。
Disadvantages of this technique include an inefficient use of network bandwidth and slow speed.
网络连接价格贵而速度慢。
在神经网络负荷预测实际应用中，突出的问题是训练样本大、训练时间长、收敛速度慢。
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
对于传统BP算法存在的收敛速度慢和易陷入局部极小值问题，人们提出了径向基函数网络。
People put forward radial basis function networks considering the conventional BP algorithm problems of slow convergence speed and easily getting into local dinky value.
由于坐标测量机几何误差变化规律复杂，采用一般的BP神经网络模型算法，速度慢且难以收敛。
Owing to the complicated variable rule of CMMs geometry error, it's difficult to convergence for using common BP neural network model arithmetic with a slow velocity.
本文针对BP神经网络收敛速度慢的缺点，提出了改进方案。
This paper puts forward an improvement plan to counter the slowness of convergence rate of BP artificial neural network.
并对以前神经网络中的两个难点：局部极小和收敛速度慢的问题进行分析。
Our previous work has shown that the network was effective in improving two difficulties, a convergence to local minimal and a slow learning speed.
针对BP算法收敛速度慢的特点，在隐含层上加入了关联节点，改善了网络的学习速率和适应能力。
Aiming at the slow convergence rate of BP neural network, append a correlative node on hidden layer, improve the adaptive ability and rate of studying of neural network.
多条流入线路有分组到达，并需要同一输出线路，或是路由器处理速度慢，那么将导致网络系统拥塞。
When into line with more than one packet arrival and the need for the same output line, or router processing speed slow, then would lead to network congestion.
经验证(PSO)优化算法可以有效地克服BP神经网络存在的学习效率低，收敛速度慢以及容易陷入局部极小点等固有缺点。
It is confirmed that PSO could overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate, being easy to fall into local minima, etc.
然而基于梯度下降的BP网络存在收敛速度慢、易陷入局部极小的缺陷。
However, BP network with gradient descent has some defects such as low convergence speed, fall in local minima.
有效地克服了人工神经网络学习速度慢、存在局部极小点的固有缺陷。
The intrinsic defects of artificial neural network, e. g. , its slow learning speed. existence of partial minimum points, are solved.
BP神经网络具有很强的映射能力，可以解决许多实际问题，但同时还存在着收敛速度慢，易陷于局部极小的缺点。
The BP neural network has the ability to solve many practical problems because of its strong mapping. However, it has slow convergence rate and is prone to fall into local extremum.
帮忙处理网络问题，例如不能上网及上网和下载速度慢等。
I could handle the problem of network. For example Internet couldn't use and the speed with upload or download in Internet etc.
同时克服了神经网络规模过于庞大及分类识别速度慢等缺点，取得了减少分类过程中模式匹配搜索量的良好效果。
At the same time, the disadvantage of the ANN scale too large and the speed of classifiable identification too slow and so on are overcome too.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
实验表明，智能神经网络系统原理为克服传统神经网络收敛速度慢的缺点，同时不增加网络负担提供了一种有效方案。
The experiment shows that INNS provides a way to accelerate the astringent speed by constructing a complicate intelligent neural network based on simple networks and by adding some rules.
鉴于BP网络存在着学习过程收敛速度慢、网络容错能力差的缺点，本文提出一种自构神经网络算法。
Self-configuring neural network is used because BP neural network has both slow convergence in learning course and poor fault tolerant ability.
但在网络运行过程中，普遍存在着网络运行速度慢问题，解决这一问题势在必行。
However, during the period when network operates, there still remains a problem that the network's speed is extensively slow. To solve this problem is vital and of an urgent tendance.
先对传统的BP人工神经网络进行了分析，针对其收敛速度慢，存在局部极小值的缺点提出了一种改进后的BP人工神将网络。
An improved BP neural network is proposed for the purpose of overcoming the slow convergence and existence of local minimum in conventional BP neural network.
先对传统的BP人工神经网络进行了分析，针对其收敛速度慢，存在局部极小值的缺点提出了一种改进后的BP人工神将网络。
An improved BP neural network is proposed for the purpose of overcoming the slow convergence and existence of local minimum in conventional BP neural network.
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