A Neuro-Fuzzy Inference System Optimized by Deterministic Annealing
Jacek M. Łęski, E. Czogała · 2000
In this paper artificial neural network based fuzzy inference system (ANNBFIS) learned by deterministic annealing has been described. The system consists of the moving fuzzy consequent in if-then rules. The location of this fuzzy set is determined by a linear combination of system inputs. This system also automatically generates rules from numerical data. The proposed system operates with Gaussian membership functions in premise part. Parameter estimation has been made by connection of both deterministic annealing and least squares methods. For initialization of unknown parameter values of premises, a preliminary fuzzy c-means clustering method has been employed. The application to prediction of chaotic time series is considered in this paper. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.