ai系統架構

The Fuzzy Logic System is a system which uses Fuzzy logic for reasoning. Fuzzy Logic is a very efficient method for performing human-like reasoning in conditions with uncertainty.

模糊邏輯系統是使用模糊邏輯進行推理的系統。 模糊邏輯是一種在不確定條件下執行類似人的推理的非常有效的方法。

If we take a look at the architecture of the Fuzzy Logic system, then we find that it is composed of the following four major parts:

如果我們看一下模糊邏輯系統的體系結構 ，那么我們發現它由以下四個主要部分組成：

Knowledge Base

知識庫

Fuzzification Module

模糊化模塊

Inference Engine

推理機

Defuzzification Module

解模糊模塊

Now, let us have a look at each of them one by one:

現在，讓我們逐一查看它們：

1)知識庫 (1) Knowledge Base)

Every system which works on Artificial Intelligence has a Knowledgebase. The Fuzzy logic system is also an AI-based system, and thus it also has its own knowledge base where all the information and data for the reference by the agent is stored. In the Knowledge Base of Fuzzy Logic system, the rules of the Fuzzy Logic set theory are stored. Their rules are present in the form of an if-else ladder. So, whenever the system tries to solve any problem, this if-else ladder is executed and the system then works on the rule that it gets from the matched condition.

每個在人工智能上運行的系統都有一個知識庫。 模糊邏輯系統也是基于AI的系統，因此它也具有自己的知識庫，該知識庫存儲了供代理參考的所有信息和數據。 在模糊邏輯系統的知識庫中，存儲了模糊邏輯集合論的規則。 他們的規則以if-else階梯的形式出現。 因此，每當系統嘗試解決任何問題時，都會執行此if-else梯形圖，然后系統根據從匹配條件獲得的規則進行工作。

2)模糊化模塊 (2) Fuzzification Module)

The fuzzification module performs the conversion of the input information. The information is converted into a form which the system can search for in its Knowledge Base. This is done by splitting the sentences into simpler terms and extracting the main terms out of it which are then sent to the inference engine for further processing.

模糊化模塊執行輸入信息的轉換。 信息將轉換為系統可以在其知識庫中搜索的形式。 這是通過將句子分成更簡單的術語并從中提取主要術語來完成的，然后將其發送到推理引擎進行進一步處理。

3)推理機 (3) Inference Engine)

The Inference engine is the main component of the Fuzzy Logic System. If compared with the computer parts, our inference engine is the same as the processor of the computer. All the processing of the information takes place inside it. The task of the inference engine is to draw a valid result by analyzing and concluding all the information that it gets from the fuzzification module. This is again done by referring to the rules and prior information present in the Knowledge Base. The final conclusions made are then sent for further modification to the defuzzification module.

推理引擎是模糊邏輯系統的主要組件。 如果與計算機部件進行比較，我們的推理引擎與計算機的處理器相同。 信息的所有處理都在其中進行。 推理引擎的任務是通過分析和總結從模糊化模塊獲得的所有信息來得出有效的結果。 再次通過參考知識庫中的規則和現有信息來完成此操作。 然后將得出的最終結論發送給去模糊化模塊進行進一步修改。

4)解模糊模塊 (4) Defuzzification Module)

The Defuzzification Module receives the processed information from the Inference Engine. This information contains the conclusion, but still, it is not in the form in which it was received, i.e. user-understandable form. So, the defuzzification module again converts this information into a form which is well accepted by the user.

去模糊化模塊從推理引擎接收處理后的信息。 該信息包含結論，但仍然不是以其接收的形式，即用戶可理解的形式。 因此，去模糊化模塊再次將該信息轉換為用戶很好接受的形式。

翻譯自: https://www.includehelp.com/ml-ai/fuzzy-logic-system-architecture-in-artificial-intelligence.aspx

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