More specifically, the Radial Basis Function (RBF) neural network architecture serves as the nonlinear modeling tool, by exploiting the simplicity of its topology and the fast fuzzy means training algorithm.
The improved FD methods comes with two the main novelty aspects: (1) the development of an enhanced optimization scheme for fuzzy systems training which builds upon the SparseFIS (Sparse Fuzzy Inference Systems) approach and enhances it by embedding genetic operators for escaping local minima ⟶ a hybrid memetic (sparse) fuzzy modeling approach, termed as GenSparseFIS.
The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set.
As can be seen, the obtained correlation coefficient values were more than 0.99, demonstrating that the ANFIS model predicted the measured data satisfactorily, and that the neuro-fuzzy model training was successfully accomplished.
The proposed methodology combines a static measurement, such as the result of a fuzzy classifier trained with historical process data, and an estimation algorithm based on Markov's theory for discrete event systems.
Dickerson and Kosko (1996), Mitaim and Kosko (2011), Huang and Xing (2002), and Yan (2010) have had such studies that confirm the possibility of extracting fuzzy-rules from training data by integrating fuzzy systems with ANNs.
The adaptive neural-fuzzy inference system training datasets are extracted from the fuzzy logic controller model developed in MATLAB Simulink and its robustness has been verified experimentally under different measurement noises and disturbances.
Second, the fuzzy trend labeled training data set is constructed based on fuzzy logic relationships and fuzzy trends of historical samples.
The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids.
PSO has been successfully applied in many areas: function optimization, artificial neural network training, fuzzy system control, and other areas where GA can be applied [14].
