Local neurofuzzy modelling

Abstract

The previously described model construction algorithms of Chapter 5 resolved model complexity by utilising the divide and conquer strategy, by decomposing high dimensional problems into a number of lower dimensional submodels each with variable dependencies whose composite solution yields the original complex problem. Central to the conventional neurofuzzy approach is an orthogonal axis partitioning of the input space, which is the main cause of the curse of dimensionality. Clearly, other decompositions or partitioning are possible including irregular simplexes (see Figure 6.1(a)) and data clustering with centred Gaussians (see Figure 6.1(b)).