A Hammerstein Neuro-Fuzzy Network with an Online Hybrid Construction Algorithm for Dynamic Applications

This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic "dynamic" rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.