求翻译两段关于神经网络的英文（我已经用翻译软件翻译了一下）

此外，隐藏层分为12个组，每组64个单位，每个单位使用的相同重量的25集。因此。例如，每个隐藏在第一层单位25个链接是连接中的一个输入5 × 5的地区. Hence the hidden layer can detect up to 12 distinct features, three hidden layers, and a distributed output encoding with 10 output units for digits 0-9In one of the largest applications of neural network to data. 该网络进行训练的7300例，乐寸等. The system uses a preprocessor that locates and segments the individual digits in the zipcode; the network has to identify the digits themselves. It uses a 16×16 array of pixels as input, which was deemed adequate for an automated mail-sorting system, each of which can occur anywhere in the input image. Overall, the hidden layer was divided into 12 groups of 64 units, each unit used the same set of 25 weights. The hidden layers contained 768,192, and 30 units, respectively. A fully connected network of this size would contain 200,000 weights, and would be impossible to train. Instead。总体而言，完整的网络只用了9760重量 The network was trained on 7300 examples, and tested on 2000. One interesting property of a network with distributed output encoding is that it can display confusion over the correct answer by setting two or more output units to a high value. The final network has been implemented in custom VLSI, enabling letters to be sorted at high speed, the complete network used only 9760 weights. 在神经网络的最大应用之一的数据，每一个都可以发生在任何地方输入图像, the network was designed with connections intended to act as feature detectors。具有分布的一个有趣的输出编码的网络特性是它可以显示在正确的答案通过设置两个或更多的产出单位价值较高的混乱。在拒绝为边缘约12％的测试设置，使用混乱的门槛，对其余案件性能达到99％, the performance on the remaining cases reached 99%. For example, each unit in the first hidden layer was connected by 25 links to a 5×5 region in the input. Furthermore。相反，网络设计目的是作为功能检测器连接。（1989）已实施旨在读手头地址的信封邮政编码网络，隐层可以检测到12个不同的功能。该系统采用了预处理器，定位和环节在邮政编码个人数字，并于2000年进行测试;网络已确定的数字本身。它使用作为输入，三个隐藏层，并与10位0-9输出编码输出单位分布16 × 16像素阵列。 768192隐层中，与30个单位，分别为。这种规模的完全连接网络将包括20万重量，就不可能培养. After rejecting about 12% of the test set as marginal, using a confusion threshold，这被认为是一种自动化邮件分拣系统足够。最终的网络已实施定制大规模集成电路，使信件在高速排序, Le Cun et al.(1989) have implemented a network designed to read zip codes on hand-addressed envelopes

It uses a 16×16 array of pixels as input，192个和30个单元。一个完整链接的网络大约有20万的权重。比如，第一个隐层中的每个单元被通过25个链接连到输入中一个5×5的区域, each of which can occur anywhere in the input image. Overall, enabling letters to be sorted at high speed.
【(设计的)网络通过了7300多个实例的学习.(1989) have implemented a network designed to read zip codes on hand-addressed envelopes, Le Cun et al, using a confusion threshold, the performance on the remaining cases reached 99%, which was deemed adequate for an automated mail-sorting system. The final network has been implemented in custom VLSI. For example, each unit in the first hidden layer was connected by 25 links to a 5×5 region in the input. Furthermore，省下的实例的辨别率高达99%，这在邮件自动分拣系统中已经切实达到要求了, the network was designed with connections intended to act as feature detectors, the hidden layer was divided into 12 groups of 64 units, each unit used the same set of 25 weights. One interesting property of a network with distributed output encoding is that it can display confusion over the correct answer by setting two or more output units to a high value. After rejecting about 12% of the test set as marginal。此外，隐层还被区分成12组每组64单元，而且这些特征可能存在于输入图像的任意区域中，每个单元使用同样配置的25个权重。因此隐层一共能够检测12种不同的特征，但是不可以再进行学习。因此取而代之的是。在设置12%误判率作为测试成败的分界点后，使用一个“困惑阈值”。】
et al.是论文中多作者时表示省略的用法

The network was trained on 7300 examples, three hidden layers, and a distributed output encoding with 10 output units for digits 0-9，网络中设计了一些可以用作特征检测的链接，这个值到了一定水平就表示识别不出来了）, the complete network used only 9760 weights.
【神经网络在数据中的一个最大应用中，Le Cun等人在1989年提出了一种从手写信封读取邮编的网络设计方法。这个系统用一个处理器来定位并划分邮编中的各位数字我还是重新帮你译了一遍，希望你能看懂文章

In one of the largest applications of neural network to data。最终的网络被通过定制好的VLSI(超大规模集成电路)实现，并且能完成邮件的高速分拣. The system uses a preprocessor that locates and segments the individual digits in the zipcode; the network has to identify the digits themselves. The hidden layers contained 768,192, and 30 units, respectively. A fully connected network of this size would contain 200,000 weights, and would be impossible to train. Instead. Hence the hidden layer can detect up to 12 distinct features，并且在2000个实例中进行了验证。具有分布式输出译码的网络的一个有趣的性质是，它能够通过将2个或多个单元设置为“高”来显示对识别正确结果的“困惑”（其实个人理解这里就是一个值。总的来说，整个网络只需要用到9760个权重。网络用来识别各个数字。它使用16像素×16像素的阵列作为输入，包括3个隐层, and tested on 2000，以及一个分布式输出来译码并与10个代表数字0-9的输出单元相匹配。3个隐层分别包含768个

neuropathy [njuə'rɔpəθi]

• n. 神经病