ANNs are an example of a "learning" system, one that "trains" on input data (in some domain) in order to effectively classify (unseen) data in that domain. They've been used for everything from character recognition to computer games and beyond.
If you're trying to find a domain, pick some topic or field that interests you, and see what kinds of classification problems exist there.
Most often for classifying noisy inputs into fixed categories, like handwritten letters into their equivalent character, spoken voice into phonemes, or noisy sensor readings into a set of fixed values. Usually, the set of categories is small (23 letters, couple of dozen phonemes, etc.)
Others will point out how all these things are better done with specialized algorithms....
I once wrote an ANN to predict the stock market. It succeeded with about 80% accuracy.
The cue here was to first get hold of a couple of million rows of real stock data. I used this data to train the network and prime it for real data. There were about 8-10 input variables and a single output value that would indicate the predicted value of the stock on the next day.
You could also check out the (ancient) ALVINN network where a car learnt to drive by itself by observing road data when a human driver was behind the wheel.
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人工神经网络是“学习”系统的一个示例，该系统对输入数据（在某些领域）进行“训练”，以便有效地对该领域中的（看不见的）数据进行分类。它们已被用于从字符识别到电脑游戏等各种领域。
如果您想找到一个领域，请选择您感兴趣的一些主题或领域，然后查看那里存在哪些类型的分类问题。
ANNs are an example of a "learning" system, one that "trains" on input data (in some domain) in order to effectively classify (unseen) data in that domain. They've been used for everything from character recognition to computer games and beyond.
If you're trying to find a domain, pick some topic or field that interests you, and see what kinds of classification problems exist there.
最常用于将噪声输入分类为固定类别，例如将手写字母分类为其等效字符，将口语语音分类为音素，或者将噪声传感器读数分类为一组固定值。通常，类别集很小（23 个字母、几十个音素等），
其他人会指出如何使用专门的算法更好地完成所有这些事情......
Most often for classifying noisy inputs into fixed categories, like handwritten letters into their equivalent character, spoken voice into phonemes, or noisy sensor readings into a set of fixed values. Usually, the set of categories is small (23 letters, couple of dozen phonemes, etc.)
Others will point out how all these things are better done with specialized algorithms....
我曾经写过一个人工神经网络来预测股市。它成功了，准确率约为 80%。
这里的提示是首先获取几百万行的真实股票数据。我使用这些数据来训练网络并为其准备真实数据。大约有 8-10 个输入变量和一个输出值，该输出值表示第二天股票的预测值。
您还可以查看（古代）ALVINN 网络，当人类驾驶员坐在方向盘后面时，汽车通过观察道路数据来学习自动驾驶。
人工神经网络也广泛应用于生物信息学。
I once wrote an ANN to predict the stock market. It succeeded with about 80% accuracy.
The cue here was to first get hold of a couple of million rows of real stock data. I used this data to train the network and prime it for real data. There were about 8-10 input variables and a single output value that would indicate the predicted value of the stock on the next day.
You could also check out the (ancient) ALVINN network where a car learnt to drive by itself by observing road data when a human driver was behind the wheel.
ANNs are also widely used in bioinformatics.