论文标题
时间序列预测的模糊导数模型方法
A fuzzy derivative model approach to time-series prediction
论文作者
论文摘要
本文提出了一种模糊的系统方法，用于预测非线性时间序列和动态系统。为此，为了捕获时间序列的行为以及有关其连续的时间派生词的信息，可以理解管理时间序列的基本机制。预测任务是由基于提取的规则和泰勒ode求解器的模糊预测器执行的。该方法已应用于基准问题：Mackey-Glass混沌时间序列。此外，进行了与其他模糊和神经网络预测指标的比较研究，这些研究表明，此处提出的方法的表现相等甚至更好。
This paper presents a fuzzy system approach to the prediction of nonlinear time-series and dynamical systems. To do this, the underlying mechanism governing a time-series is perceived by a modified structure of a fuzzy system in order to capture the time-series behaviour, as well as the information about its successive time derivatives. The prediction task is carried out by a fuzzy predictor based on the extracted rules and on a Taylor ODE solver. The approach has been applied to a benchmark problem: the Mackey- Glass chaotic time-series. Furthermore, comparative studies with other fuzzy and neural network predictors were made and these suggest equal or even better performance of the herein presented approach.
