A powerful algorithm is introduced to build an adaptive Takagi-Sugeno
neurofuzzy model online from zero fuzzy rules for unknown nonlinear systems.
The proposed technique creates the fuzzy rules and adapts the membership
functions in the IF statement as well as the linear model in the THEN statements
in an automated manner online. In addition, the algorithm searches for
redundant rules to be eliminated to get less number of rules as possible. Based on
the neurofuzzy model an adaptive controller is built and the proposed technique
has been applied to nonlinear plant models commonly encountered in chemical
reactors to elaborate its efficiency, thus showing the powerful behavior of the
proposed controller. A stability study is also included.