Hierarchical Recurrent Neuro-Fuzzy Systems

Andreas Nürnberger1
(Professor Lotfi A. Zadeh)
BTexact Technologies, UK and Berkeley Initiative in Soft Computing (BISC)

Fuzzy systems, neural networks, and their combination in neuro-fuzzy systems are already well established in data analysis and system control. Neuro-fuzzy systems are especially well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a priori knowledge into the process of data analysis. However, its recurrent variants, especially recurrent neuro-fuzzy models, are still rarely used.

To simplify the definition of fuzzy systems, or to reduce its complexity, hierarchical and recurrent structures can be used. Thus, more transparent rule bases that are also easier to maintain can be designed. These structures also allow the use of time delayed input or reuse of time delayed output from the fuzzy system itself. Thus, we obtain a rule base that is able to describe dynamic behavior.

In this project we study the capabilities of hierarchical recurrent neuro-fuzzy models. Furthermore, we have developed a neuro-fuzzy model that can be used to learn and optimize hierarchical recurrent fuzzy rule bases from data.

A. Nürnberger and R. Kruse, "A Neuro-Fuzzy Approach to Optimize Hierarchical Recurrent Fuzzy Systems," Fuzzy Optimization and Decision Making, Vol. 1, No. 2, 2002.
A. Nürnberger, "Using Hierarchical Recurrent Neuro-Fuzzy Systems for Decision Making," Information Refinement and Revision for Decision Making, AAAI Spring Symp., Technical Report SS-02-03, AAAI Press, Menlo Park, CA, 2002.
1Postdoctoral Researcher

More information (http://nuernberger.webhop.org/nfs) or

Send mail to the author : (anuernb@eecs.berkeley.edu)

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