使用 MATLAB 拟合神经网络
我想使用神经网络拟合一个函数，以 0/1 作为输出。 请帮助我找到最好的方法。
事实上我想知道MATLAB中的拟合函数，特别是神经网络工具箱中的拟合函数。我不知道哪种方法适合对具有二进制输出的函数进行建模。
另外，在 MATLAB 中是否可以增加体重？
I want to fit a function using neural networks, with 0/1 as outputs.
please help me find the best way to it.
In fact I want to know the fitting function in MATLAB, specifically in the neural network toolbox. I don't know which method is good for modeling a function with binary output.
Also is there anyway in MATLAB that I can gain weights?
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如果您有一个二元分类任务（0/1输出），那么您可以训练一个神经网络有两个输出节点，每个类一个，技巧是在输出节点，使其始终在
[0,1]范围内（如果您使用NEWPR函数，这是默认值）。它可以解释为概率，那么您可以使用默认值0.5作为阈值，或者使用 ROC 曲线 为您的案例找到更好的阈值。请参阅这篇文章了解 MATLAB 中的简单示例学习 XOR 二元运算符。
If you have a binary classification task (0/1 output), then you can train a NN with two output nodes, one per class, and the trick is to use a logistic function on the output node so that that its always in the range
[0,1](this is the default if you useNEWPRfunction). It can be interpreted as a probability, then you can use the default value0.5as threshold or maybe use ROC curves to find a better threshold for your case.Please see this post for a simple example in MATLAB of learning the XOR binary operator.
通常神经网络用于决策，因此 0/1 答案正是您想要的：“是”或“否”。
为了实际适应网络，需要使用两组已知的可产生“是”和“否”答案的组。然后计算I 类和 II 类错误的比率（通常称为纯度和 1-效率） ）作为网络参数的函数。
这些函数的值介于 0 和 1 之间。拟合的目标是找到一组网络参数，在 I 类和 II 类误差之间产生可接受的平衡，同时使它们尽可能小。
Usually neural networks are used for decision making, so a 0/1 answer is just what you want: a "yes" or a "no".
To actually fit the network one uses two sets that are known to produce a "yes" and "no" answer. Then one calculates the rates of Type I and Type II errors (often called purity and 1-efficiency) as a function of the network parameters.
These functions have values between 0 and 1. The goal of the fit is to find a set of network parameters which produce an acceptable balance between Type I and Type II errors while making them both as small as possible.