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A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology

机译：基于分层知识的语言建模环境：模型和迭代方法

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摘要

Although linguistic models are highly descriptive, they suffer from inaccuracy in some complex problems. This fact is due to problems related to the inflexibility of the linguistic rule structure that has been considered. Moreover, methods often employed to design these models from data are also biased by the former structure and by their nature, which is close to prototype identification algorithms. In order to deal with these problems of linguistic modeling, an extension of the knowledge base of linguistic fuzzy rule-based systems was previously introduced, i.e., the hierarchical knowledge base (HKB) (IEEE Trans. Fuzzy Systems 10 (1) (2002) 2). Hierarchical linguistic fuzzy models, derived from this structure, are viewed as a class of local modeling approaches. They attempt to solve a complex modeling problem by decomposing it into a number of simpler linguistically interpretable subproblems. From this perspective, linguistic modeling using an HKB can be regarded as a search for a decomposition of a non-linear system that gives a desired balance between the interpretability and the accuracy of the model. Using this approach, we are able to effectively explore the fact that the complexity of the systems is usually not uniform. We propose a well-defined hierarchical environment adopting a more general treatment than the typical prototype-oriented learning methods. This iterative hierarchical methodology takes the HKB as a base and performs a wide variety of linguistic modeling. More specifically, from fully interpretable to fully accurate, as well as intermediate trade-offs, hierarchical linguistic models. With the aim of analyzing the behavior of the proposed methodology, two real-world electrical engineering distribution problems from Spain have been selected. Successful results were obtained in comparison with other system modeling techniques.
机译：尽管语言模型具有很高的描述性，但它们在某些复杂的问题中存在不准确的问题。这个事实是由于与已经考虑的语言规则结构的不灵活性有关的问题。此外，通常用于根据数据设计这些模型的方法也因前者的结构及其性质而存在偏差，这与原型识别算法很接近。为了解决语言建模的这些问题，先前引入了基于语言模糊规则的系统的知识库的扩展，即分层知识库（HKB）（IEEE Trans.Fuzzy Systems 10（1）（2002）） 2）。从这种结构派生的分层语言模糊模型被视为一类局部建模方法。他们试图通过将复杂的建模问题分解为许多在语言上可解释的子问题来解决该问题。从这个角度来看，使用HKB进行语言建模可以看作是对非线性系统分解的一种搜索，该非线性系统可以在模型的可解释性和准确性之间取得理想的平衡。使用这种方法，我们能够有效地探索系统复杂性通常不统一的事实。我们提出了一个定义明确的分层环境，该环境比典型的面向原型的学习方法要采用更一般的处理方式。这种迭代的分层方法以HKB为基础，并执行各种语言建模。更具体地说，从完全可解释到完全准确以及中间的权衡，分层语言模型。为了分析所提出方法的行为，选择了西班牙的两个现实世界的电气工程分配问题。与其他系统建模技术相比，获得了成功的结果。

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