Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations.
Hao HuangSung-Kwun OhChuan-Kun WuWitold PedryczPublished in: Fuzzy Sets Syst. (2022)
Keyphrases
- fuzzy clustering
- iterative learning
- support vector
- kernel function
- polynomial kernels
- kernel methods
- iterative learning control
- fuzzy c means
- fuzzy clustering algorithm
- fuzzy clustering algorithms
- incremental learning
- fuzzy c means algorithm
- support vector machine
- error reduction
- membership functions
- feature selection
- fuzzy model
- cluster analysis
- fuzzy clustering method
- clustering algorithm
- feature space
- hyperparameters
- cross validation
- classification accuracy
- training examples
- high dimensional
- data analysis
- neural network
- possibilistic clustering