Improved fuzzy lattice neurocomputing (FLN) for semantic neural computing

This work, first, shows the inherent capacity of neural net /spl sigma/-FLNMAP for classification based on semantics and, second, it demonstrates the capacity of an ensemble of /spl sigma/-FLNMAP voters to improve classification accuracy. The /spl sigma/-FLNMAP neural network is presented here as a tool for function approximation. New definitions and useful properties extend coherently the applicability of /spl sigma/-FLNMAP. An ensemble of /spl sigma/-FLNMAP voters is treated as a statistical model whose parameters can be estimated from the training data. Noise canceling effects are discussed. Experimental results in four classification problems compare favorably with results by alternative classification methods from the literature.