基于全因子设计的复杂系统ANFIS模型

M. Buragohain, C. Mahanta
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引用次数: 3

摘要

在本文中,我们提出了一个基于自适应网络的模糊推理系统(ANFIS)模型,其中用于训练的数据对的数量被称为全因子设计的工程统计技术的应用最小化。将该方法应用于Box和Jenkins煤气炉的基准数据进行了实验验证。通过采用我们提出的方法,与传统的ANFIS方法相比,在ANFIS网络中学习所需的数据数量可以显著减少。将该方法与传统ANFIS网络的结果进行了比较。结果表明,该模型与传统的ANFIS模型具有较好的一致性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Full Factorial Design Based ANFIS Model for Complex Systems
In this paper we propose an adaptive network based fuzzy inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced as compared to the number of data required for the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model
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