Neuro-Chaotic Fuzzy Sets and Systems
Seyyedh Fatemeh Molaeezadeh, Mohammad Hassan Moradi · Computational Intelligence in Electrical Engineering · 2014
This paper presents new fuzzy sets by imitating from the neuronal structure and the fuzziness and chaotic dynamics in human brain. To constitute the proposed sets, coupled chaotic oscillators are utilized. The major advantage of these sets, in comparison with the existing fuzzy sets, is the bifurcation capability. This property enables the sets to create various fuzzy sets, such as type-1 or type-2 fuzzy sets convex or non-convex fuzzy sets etc. To evaluate the proposed sets in modeling uncertainties, a framework is presented to design neuro-chaotic fuzzy systems, and then it is applied to the problem of forecasting Mackey-Glass time series that is corrupted by an additive noise with certain SNRs. Results show that the proposed system, in comparison with type-1 and interval type-2 neuro-fuzzy systems, has lower prediction error for both training and test datasets.