贝叶斯网络和模糊逻辑

发布于 2024-12-03 07:45:59 字数 81 浏览 2 评论 0原文

谁能给我一个在入侵检测中使用贝叶斯网络和模糊逻辑的例子？

我正在努力弄清楚如何使用它。还有上面有代码吗？

谢谢你们。

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此刻的回忆 2024-12-10 07:45:59

确切的细节取决于您谈论的是防盗警报类型的情况（传感器读数）还是涉及保安人员和激光鲨鱼的更奇特的情况。无论哪种方式，原理都是一样的。

您从描述影响入侵的基本事物的根节点开始，例如，

Sensor detected motion (true/false)  
Shark smelt blood (true/false)
Temperature (too low/just right/too high)
Security guard is asleep
...  
any other things you can think of.

您为每个根节点的每个状态分配一个概率。

P(Security guard is asleep) = 0.25

然后，您定义依赖于这些根节点的子节点，例如，保安听到噪音将依赖于保安正在睡觉。

给定其父节点的每个状态，为子节点的每个状态分配条件概率。

P(Security guard heard noise|Security guard is asleep) = 0.05
P(Security guard heard noise|Security guard is not asleep) = 0.5

最终，您会希望得到像盗窃已被挫败这样的结果。

设置网络节点后，您可以对其进行评估，并计算不同结果发生的概率。

接下来你添加证据。因此，如果您知道鲨鱼闻到了血的味道，该节点就会设置为特定值，您可以重新评估网络以查看概率如何变化。

在软件方面，Bayes Net 工具箱备受好评。

The exact details will depend upon whether you're talking about a burglar alarm type situation (sensor readings) or something fancier involving security guards and sharks with lasers. Either way, the principle is the same.

You start with root nodes describing the basic things that affect intrusion, e.g.,

Sensor detected motion (true/false)  
Shark smelt blood (true/false)
Temperature (too low/just right/too high)
Security guard is asleep
...  
any other things you can think of.

You assign a probability to each state of each root node.

P(Security guard is asleep) = 0.25

Then you define child nodes that depend upon those root nodes, e.g., Security guard heard noise would depend upon Security guard is asleep.

You assign conditional probabilities for each state of the child nodes, given each state of its parents.

P(Security guard heard noise|Security guard is asleep) = 0.05
P(Security guard heard noise|Security guard is not asleep) = 0.5

Eventually, you'll want to get to an outcome like Burglary has been foiled.

Once you have your network node set up, you can evaluate it, and calculate the probability of different outcomes happening.

Next you add evidence. So if you know your shark smelt blood, that node gets set to a particular value and you can reevaluate the network to see how probabilities have changed.

In terms of software, the Bayes Net toolbox is well regarded.

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