模糊邏輯系統

As discussed earlier, the Fuzzy Logic System consists of 4 components: the Knowledge Base, Fuzzification Module, Inference Engine, and the Defuzzification Module. We know how the data and information flow between these components, but we do not know how the processing of that information takes place. Here, we are going to study the same.

如前所述， 模糊邏輯系統由4個組件組成：知識庫，模糊化模塊，推理引擎和模糊化模塊。 我們知道數據和信息在這些組件之間的流動方式，但是我們不知道該信息的處理方式。 在這里，我們將進行同樣的研究。

會員功能 (Membership Function)

The membership function is the backbone of the Inference Engine. It is a function which quantifies the data and represents a Fuzzy Set, which is defined over the range 0 to 1 (both inclusive). The input space that the Membership Function works in is known as the Universe of Discourse and the data that it takes as input are usually linguistic terms.

隸屬函數是推理引擎的基礎。 它是一種量化數據并表示模糊集的函數，該模糊集在0到1(包括兩者)的范圍內定義。 隸屬函數在其中起作用的輸入空間被稱為“話語宇宙”，而它作為輸入的數據通常是語言術語。

The Linguistic terms can be defined as the words which define the physical characteristics of a function. For example, if we are defining the temperature of a body, then we use the terms which define the characteristics of it, like high, low, very high, moderate, etc. These are the linguistic terms here.

語言術語可以定義為定義功能的物理特征的單詞。 例如，如果我們要定義一個物體的溫度，那么我們將使用定義該物體特征的術語，例如高，低，非常高，中等等。這些是這里的語言術語。

The Membership function can be defined as: μ_P: X → [0, 1]

隸屬度函數可以定義為： μ _P ：X→[0，1]

Where,

哪里，

• 'μ' denotes the membership function,

  “ μ”表示隸屬函數，

• 'P' denoted the Fuzzy Set,

  “ P”表示模糊集，

• and 'X' denotes the universe of discourse, i.e. input space.

  “ X”表示話語的宇宙，即輸入空間。

Algorithm:

算法：

The algorithm on which the Fuzzy logic system is as follows:

模糊邏輯系統的算法如下：

Define the Knowledgebase by feeding the Fuzzy set Rules into it.

通過將模糊集規則輸入知識庫來定義知識庫。

Define the universe of discourse for the membership function.

定義隸屬函數的話語范圍。

Construct the membership function (By any method, Triangular, Singleton or Gaussian).

構造隸屬函數(通過任何方法，使用Triangular，Singleton或Gaussian)。

Perform Fuzzification to convert the input information into data in the form of Fuzzy sets.

執行模糊化以將輸入信息轉換為模糊集形式的數據。

Process the Fuzzy data set and draw the inference using the rules defined in the Knowledgebase (This process takes place inside the Inference Engine).

使用知識庫中定義的規則處理模糊數據集并得出推理(此過程在推理引擎內部進行)。

Perform Defuzzification to convert the fuzzy data into the user-understandable form and produce the output.

執行“去模糊化”以將模糊數據轉換為用戶可理解的形式并產生輸出。

翻譯自: https://www.includehelp.com/ml-ai/working-inside-the-fuzzy-logic-system-artificial-intelligence.aspx

模糊邏輯系統

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