Using multi-layer percetron fuzzy adaptive filter in non-linear channel equalisation

An existing non-linear filter like the recursive least square fuzzy adaptive filter (RLS-FAF) is developed for channel equalisation. It makes use of both the linguistic and numerical information in their natural form. It is constructed from a set of changeable fuzzy IF-THEN rules which comes from the human experts. This equaliser has the ability to converge to its optimum decision region in a shorter period. The other advantage of the FAF is that linguistic information from the human experts can be directly incorporated into the filters. However, the RLS-FAF is not without limitations. First, it requires a high computational load and secondly, it requires prior linguistic knowledge or human experts. Therefore, a new architecture, namely the multilayer perceptron fuzzy adaptive filter (MLP-FAF) is implemented for the non-linear adaptive equalisation specifically in a digital communication system. This approach replaces the human experts with a MLP structure. The MLP-FAF described has the added advantage over the RLS-FAF in the reduced computational complexity due to the usage of the LMS algorithm in updating the fuzzy rules.