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A GA-based fuzzy modeling approach for generating TSK models

机译：一种基于GA的模糊建模方法，用于产生TSK模型

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This paper proposes a new genetic-based modeling method for building simple and well-defined TSK models with scattcr-type input partitions. Our approach manages all attributes characterizing the structure of a TSK model, simultaneously. Particularly, it determines the number of rules, the input partition, the participating inputs in each rule and the consequent parameters. The model building process is divided into two phases. In phase one, the structure learning task is formulated as a multi-objective optimization problem which is resolved using a novel genetic-based structure learning (GBSL) scheme. Apart from the mean square error (MSE) and the number of rules, three additional criteria are introduced in the fitness function for measuring the quality of the partitions. Optimization of these measures leads to models with representative rules, small overlapping and efficient data cover, in order to obtain models with accurate data fitting and good local performance, the consequent parameters are determined using a local MSE function while the overall model is evaluated on the basis of a global MSE function. The search capabilities of the suggested structure learning scheme arc significantly enhanced by including a highly effective local search operator implemented by a micro-genetic algorithm and four problem-specific operators. Finally, a genetic-based parameter learning (GBPL) scheme is suggested in phase two, which performs fine-tuning of the initial models obtained after structure learning. The performance of the proposed modeling approach is evaluated using a static example and a well-known dynamic benchmark problem. Simulation results demonstrate that our models outperform those suggested by other methods with regard to simplicity, model structure, and accuracy.
机译：本文提出了一种新的基于基于遗传的模型，用于构建具有Scattcr型输入分区的简单和明确定义的TSK模型。我们的方法同时管理表征TSK模型结构的所有属性。特别地，它确定规则的数量，输入分区，每个规则中的参与输入以及随后的参数。模型构建过程分为两个阶段。在第一阶段，结构学习任务被制定为使用基于新的基于遗传的结构学习（GBSL）方案来解决的多目标优化问题。除了均方误差（MSE）和规则的数量之外，在适合功能中引入了三个附加标准，以测量分区的质量。这些措施的优化导致具有代表性规则的模型，小重叠和高效的数据封面，以便获得具有准确的数据拟合和良好局部性能的模型，因此使用本地MSE函数确定在整体模型上进行了评估全球MSE函数的基础。建议结构学习方案的搜索能力通过包括由微遗传算法和四个问题特定运算符实现的高效本地搜索操作员显着增强。最后，在第2阶段建议基于遗传的参数学习（GBPL）方案，其执行结构学习后获得的初始模型的微调。使用静态示例和众所周知的动态基准问题评估所提出的建模方法的性能。仿真结果表明，我们的模型优于其他方法，以了解简单，模型结构和准确性。

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