Complexity reduction of singleton based neuro-fuzzy algorithm

During the past few years, efficient singular value-based complexity reduction tools have been developed for fuzzy logic techniques. The paper introduces a singular value-based reduction method to the generalised type neural network. The method conducts singular value decomposition of the weighting functions defined on the connections among the neurons and generates certain linear combinations of the original weighting functions to form a new connection-net for the complexity reduced neural network