A Novel Parameter Estimation Fuzzy Network for Target Approximation

Here we introduce a novel Neuro-Fuzzy system for approximation and prediction of complex plants. The proposed system uses parameter estimation and scaling techniques; so called PENTA which stands for Parameter Estimation Network for Target Approximation. PENTA consists of two major components: a scaling component and a parameter estimation unit. The first component provides the gains required by the second component which is a set of primitive functions used for scaling. So the overall output of network is a synthetic function that is linear composition of these parameters and primitive functions. These two components have been expanded through five layers of a Neuro-Fuzzy network. Experimental results show that in comparison with current Neuro-Fuzzy systems like ANFIS, this novel system can have a better adaptation to complex plants. Also results show the unique capability of this system, in consecutive prediction of chaotic systems