摘要翻译：
基于模糊规则的模型具有在紧域上以任意精度逼近任意连续函数的能力。大多数FLC设计过程依赖于经验操作者的启发式知识。为了使设计过程自动化，我们提出了一种学习模糊规则和隶属度函数参数的遗传方法。此外，利用Akaike信息准则(AIC)、Bhansali-Downham信息准则(BDIC)和Schwarz-Rissanen信息准则(SRIC)等统计信息准则，通过约简模糊规则来构造最优模糊模型。采用遗传算法设计Takagi-Sugeno-Kang(TSK)模型，用于前项规则参数的辨识和后项规则参数的辨识。计算机仿真验证了所构造的模糊控制器的性能。
---
英文标题：
《Optimal Fuzzy Model Construction with Statistical Information using
  Genetic Algorithm》
---
作者：
Md. Amjad Hossain, Pintu Chandra Shill, Bishnu Sarker, and Kazuyuki
  Murase
---
最新提交年份：
2012
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical information criteria such as the Akaike information criterion (AIC), the Bhansali-Downham information criterion (BDIC), and the Schwarz-Rissanen information criterion (SRIC) are used to construct optimal fuzzy models by reducing fuzzy rules. A genetic scheme is used to design Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule parameters and the identification of the consequent parameters. Computer simulations are presented confirming the performance of the constructed fuzzy logic controller.
---
PDF链接：
https://arxiv.org/pdf/1201.2004