A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
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This paper describes a generalized fuzzy learning machine, which is a generalized and modified type of the neo-fuzzy-neuron presented by the authors in 1992. This machine can well grasp the nonlinear correlation of each input. It has a very high nonlinear mapping ability compared with the conventional neural networks, and it guarantees the global minimum. Furthermore, learning speed and its accuracy are improved drastically. It was successfully applied to the identification of the nonlinear dynamical system, e.g. two dimensional Lorenz chaotic model, and to the automatic detection of landmark location in the roentgenographic cephalogram for orthodontic treatment. The results were promising.