Artificial Neural Network 简明教程

Applications of Neural Networks

在研究神经网络已广泛使用的领域之前，我们需要了解为什么神经网络将成为首选应用程序。

Before studying the fields where ANN has been used extensively, we need to understand why ANN would be the preferred choice of application.

Why Artificial Neural Networks?

我们需要通过人的例子来理解上述问题的答案。作为一个孩子，我们曾经在长辈的帮助下学习事物，其中包括父母或老师。然后，通过自学或实践，我们在整个生命中不断学习。科学家和研究人员也像人类一样，让机器变得智能，而神经网络由于以下原因，在其中扮演着非常重要的角色：

We need to understand the answer to the above question with an example of a human being. As a child, we used to learn the things with the help of our elders, which includes our parents or teachers. Then later by self-learning or practice we keep learning throughout our life. Scientists and researchers are also making the machine intelligent, just like a human being, and ANN plays a very important role in the same due to the following reasons −

  1. With the help of neural networks, we can find the solution of such problems for which algorithmic method is expensive or does not exist.

  2. Neural networks can learn by example, hence we do not need to program it at much extent.

  3. Neural networks have the accuracy and significantly fast speed than conventional speed.

Areas of Application

以下是神经网络使用的一些领域。它表明神经网络在其开发和应用中采用跨学科的方法。

Followings are some of the areas, where ANN is being used. It suggests that ANN has an interdisciplinary approach in its development and applications.

Speech Recognition

语言在人际交往中占有突出地位。因此，人们自然会期望与计算机进行语音交互。在当前时代，为了与机器进行通信，人类仍然需要复杂的语言，这些语言难以学习和使用。为了消除这种交流障碍，一种简单的解决方案可能是以机器能够理解的口语进行交流。

Speech occupies a prominent role in human-human interaction. Therefore, it is natural for people to expect speech interfaces with computers. In the present era, for communication with machines, humans still need sophisticated languages which are difficult to learn and use. To ease this communication barrier, a simple solution could be, communication in a spoken language that is possible for the machine to understand.

在这一领域已经取得了很大进展，然而，此类系统仍然面临词汇或语法有限的问题，以及针对不同条件的不同说话人对系统进行再培训的问题。神经网络在这个领域发挥着重要作用。下列神经网络用于语音识别：

Great progress has been made in this field, however, still such kinds of systems are facing the problem of limited vocabulary or grammar along with the issue of retraining of the system for different speakers in different conditions. ANN is playing a major role in this area. Following ANNs have been used for speech recognition −

  1. Multilayer networks

  2. Multilayer networks with recurrent connections

  3. Kohonen self-organizing feature map

最适合这种网络的是 Kohonen 自组织特征映射，它的输入是语音波形的短片段。它会将同类音素映射到输出阵列中，称为特征提取技术。在提取特征之后，借助一些作为后端处理的声学模型，它将识别出说话内容。

The most useful network for this is Kohonen Self-Organizing feature map, which has its input as short segments of the speech waveform. It will map the same kind of phonemes as the output array, called feature extraction technique. After extracting the features, with the help of some acoustic models as back-end processing, it will recognize the utterance.

Character Recognition

这是一个属于模式识别一般领域的有趣问题。许多神经网络已被开发用于自动识别手写字符，无论是字母还是数字。以下是用于字符识别的某些 ANN −

It is an interesting problem which falls under the general area of Pattern Recognition. Many neural networks have been developed for automatic recognition of handwritten characters, either letters or digits. Following are some ANNs which have been used for character recognition −

  1. Multilayer neural networks such as Backpropagation neural networks.

  2. Neocognitron

虽然反向传播神经网络有几个隐藏层，但从一层到下一层的连接模式是局部化的。类似地，新认知网络也拥有几个隐藏层，并且它针对此类应用分层进行训练。

Though back-propagation neural networks have several hidden layers, the pattern of connection from one layer to the next is localized. Similarly, neocognitron also has several hidden layers and its training is done layer by layer for such kind of applications.

Signature Verification Application

签名是在法律交易中授权和验证某个人的最有用的方法之一。签名验证技术是一种非视觉技术。

Signatures are one of the most useful ways to authorize and authenticate a person in legal transactions. Signature verification technique is a non-vision based technique.

对于此应用，第一种方法是提取特征或代表签名的一组几何特征。利用这些特征集，我们必须使用有效的神经网络算法训练神经网络。此经过训练的神经网络在验证阶段将签名分类为真品或伪造。

For this application, the first approach is to extract the feature or rather the geometrical feature set representing the signature. With these feature sets, we have to train the neural networks using an efficient neural network algorithm. This trained neural network will classify the signature as being genuine or forged under the verification stage.

Human Face Recognition

这是识别给定人脸的生物识别方法之一。这是典型的任务，因为其表征为“非人脸”图像。但如果神经网络接受了良好的训练，那么它可以根据图像将图像分为两类，即有脸的图像和没有脸的图像。

It is one of the biometric methods to identify the given face. It is a typical task because of the characterization of “non-face” images. However, if a neural network is well trained, then it can be divided into two classes namely images having faces and images that do not have faces.

首先，必须对所有输入图像进行预处理。然后，必须减小该图像的维数。最后，必须使用神经网络训练算法对其进行分类。以下神经网络用于使用经过预处理的图像进行训练目的 −

First, all the input images must be preprocessed. Then, the dimensionality of that image must be reduced. And, at last it must be classified using neural network training algorithm. Following neural networks are used for training purposes with preprocessed image −

  1. Fully-connected multilayer feed-forward neural network trained with the help of back-propagation algorithm.

  2. For dimensionality reduction, Principal Component Analysis (PCA) is used.