A neuro-fuzzy approach to optimize hierarchical recurrent fuzzy systems

被引：7
|
作者：
Nürnberger A. [1 ]
Kruse R. [2 ]
机构：
[1] University of California at Berkeley, EECS, Computer Science Division, Berkeley
[2] University of Magdeburg, Faculty of Computer Science
来源：
Fuzzy Optimization and Decision Making | 2002年 / 1卷 / 2期
关键词：
Decision support; Hierarchical fuzzy system; Hybrid system; Neuro-fuzzy; Recurrent architecture;
D O I：
10.1023/A:1015739303105
中图分类号：
学科分类号：
摘要：
To simplify the definition of fuzzy systems or to reduce its complexity hierarchical structures can be used. Thus, more transparent rule bases that are also easier to maintain can be designed. Furthermore, it is sometimes necessary to use time delayed input or to reuse time delayed output from the fuzzy system itself to obtain a rule base that describes the analyzed problem appropriately. This leads to hierarchical recurrent architectures that have increased approximation capabilities since they are able to store information of the past. In this article we present a neuro-fuzzy model that can be used to optimize hierarchical recurrent fuzzy rule bases if training data is available. Furthermore, we present an approach to learn initial rule bases from data using rule templates. © 2002 Kluwer Academic Publishers.
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页码：221 / 248
页数：27
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