Modeling Corrupted Time Series Data via Nonsingleton Fuzzy Logic System
Modeling Corrupted Time Series Data via Nonsingleton Fuzzy Logic System
复制标题
通过非单一模糊逻辑系统对损坏的时间序列数据进行建模
DOI：
10.1007/978-3-540-30499-9_202
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Gwi
中科院分区：
文献类型：
--
作者：
Dongwon Kim;Sung;Gwi
本文研究了用非单态模糊逻辑系统（NFLS）对受噪声干扰的时间序列数据进行建模和识别。该系统的主要特点是输入是模糊数建模的模糊系统。因此，在可用的训练数据或模糊逻辑系统的输入数据被噪声破坏的情况下，NFLS特别有用，将演示Box-Jenkin煤气炉数据的仿真结果来展示其性能。我们还比较了NFLS方法的结果与仅使用传统模糊逻辑系统的结果。因此，可以认为NFLS比传统的模糊逻辑系统在建模噪声时间序列数据方面做得更好。
This paper is concerned with the modeling and identification of time series data corrupted by noise using nonsingleton fuzzy logic system (NFLS). Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data, or the input data to the fuzzy logic system, are corrupted by noise Simulation results of the Box-Jenkin’s gas furnace data will be demonstrated to show the performance. We also compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system. Thus it can be considered NFLS does a much better job of modeling noisy time series data than does a traditional fuzzy logic system.