This paper introduces a new method to identify
the qualified rule-relevant nodes to construct hierarchical
neuro-fuzzy systems (HNFSs). After learning, the proposed
method analyzes the entire history of activities and
behaviors of all rule nodes, which reflects their levels of
involvement or contribution during the process. The less
qualified rule-relevant nodes can then be identified and
removed, reducing the size and complexity of the HNFS.
Upon the repetitive learning process, the method may be
repetitively applied until a satisfactory result is obtained,
simultaneously improving the performance and reducing
the size and complexity. Incorporated with the method is a
new HNFS architecture which addresses both the scalability
problem experienced in rule based systems and the
restriction of the ‘‘overcrowded defuzzification’’ problem
found in hierarchical designs. In order to verify the performance,
the proposed method has been successfully
tested against five well-known classification problems
whose results are provided and then discussed in the concluding
remarks
