基于Voronoi的模糊递归控制器的演化
Evolution of Voronoi based Fuzzy Recurrent Controllers
摘要：
模糊控制器通常是通过将人类专家的知识公式化为一组语言变量和模糊规则来设计的。其中最成功的方法，自动化的模糊控制器的开发过程是进化算法。在这项工作中，我们提出了递归模糊Voronoi（RFV）模型，经常性模糊系统的表示。它是由Kavka和Schoenauer提出的FV模型的扩展，扩展了应用领域，包括时间问题。FV模型是基于沃罗诺伊图的模糊控制器的表示，其可以表示具有协同规则的模糊系统，满足$ε$ -完备性并且提供了引入先验知识的简单方式。在所提出的表示中，通过包括通过将输出连接到输入来提供反馈的内部单元来嵌入时间关系。这些内部单元充当存储元件。在RFV模型中，内部单元的语义可以与先验规则一起指定。规则的几何解释允许在演化过程中使用几何变分算子。在系统辨识和进化机器人领域的两个问题中验证了表示和算法。
英文摘要：
A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the $ε$-completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics.
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