Neo-fuzzy-neuron based new approach to system modeling, with application to actual system
This paper introduces a new approach to system modeling by using a neo-fuzzy-neuron. The system of concern is modeled adaptively by simply feeding to the neo-fuzzy-neuron, the basic principle of which was proposed by the authors in 1992, the input and the output data of the objective system. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then extended in order to get a better generalization capability. An adaptive fuzzy modeling with use of a piece-wise linear membership function is also introduced. The experimental results have provided substantial proofs for their practical use.
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