SYSTEM MODELING BY A NEO-FUZZY-NEURON WITH APPLICATIONS TO ACOUSTIC AND CHAOTIC SYSTEMS
Eiji Uchino, Takeshi Yamakawa · International Journal of Artificial Intelligence Tools · 1995
This paper describes a new approach to system modeling by using a neo-fuzzy-neuron. The target system is modeled adaptively by simply feeding its input and output data to the neo-fuzzy-neuron, whose basic principle was proposed by the authors in 1992. 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 enhanced in order to get a better generalization capability. The enhanced version of the neo-fuzzy-neuron could facilitate to identify clearly the nonlinear chaotic system, and could most likely predict the future behavior of the system. The neo-fuzzy-neuron was further applied to the identification of actual sound insulation systems. The application was a success and provided promising results.