Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a neurofuzzy network model with the Mixture of Experts Network (MEN) modelling approach is established. Based on this linkage, two new neurofuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system; a conventional latticed based neurofuzzy model is initially generated followed by parsimonious model selection at each iteration using a new simple MEN construction method using a cost criterion based on the MEN model Output sensitivity to each expert. This scheme uses an extension to the well established normalised least means squares as a model parametric learning algorithm. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm, where each rule is either trained or inhibited. These two construction methods are equivalent in their effectiveness in overcoming the Curse of dimensionality by reducing the dimensionality of the regression vector. but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.