In this paper we proposed a fuzzy neural network model which can
einbody a fuzzy Takagi-Sugeno model and carry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of several new procedures námely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure Identification. A fuzzy neural network is constructed based on fuzzy model. By learning of the neural network we can tuně of embedded initial fuzzy model. To show the applicability of new method and to niake a possibility to reál systerns rnodelling, we designed the fuzzy-neural network prograrnrne tool FUZNET. Next, we perforrned numerical experiment to do fuzzy rnodelling for an artifical tirne series and reál non-linear complex systém.