ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM
ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM
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DOI：
10.1109/21.256541
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发表时间：
1993-05-01
期刊：
影响因子：
--
通讯作者：
JANG, JSR
中科院分区：
文献类型：
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作者：
JANG, JSR
提出了一种基于自适应网络的模糊推理系统ANFIS（adaptive-network-based fuzzy inference system）的结构和学习过程。通过使用混合学习过程，所提出的ANFIS可以构建一个输入输出映射的基础上，人类的知识（模糊if-then规则的形式）和规定的输入输出数据对。在仿真中，ANFIS架构被用来建模非线性函数，在线识别控制系统中的非线性元件，并预测混沌时间序列，都产生了显着的效果。与人工神经网络和早期的模糊建模工作的比较上市和讨论。建议ANFIS的其他扩展和有前途的自动控制和信号处理的应用也提出了建议。
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-linely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested.