Membership and inference rule generation for fuzzy-neural MIMO channel modeling

Membership and inference rule generation plays a critical role in performance enhancement of fuzzy-neural (FN) modeling of Multi-Input-Multi-Output (MIMO) wireless channels. This work proposes certain methods of the formation of multiple membership generation as well as some inference rule, that determine the performance of FN-MIMO modeling in terms of processing speed and precision. Experimental results establish the enhanced performance of the proposed system in comparison to statistical and other methods.