A resource-allocating network for function interpolation
Neural Computation
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A function estimation approach to sequential learning with neural networks
Neural Computation
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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In this paper, a novel paradigm, termed online self constructing fuzzy neural network with restrictive growth (OSFNNRG) which incorporates a pruning strategy into new growth criteria, is proposed. The proposed growing procedure without pruning not only speeds up the online learning process but also results in a more parsimonious fuzzy neural network while comparable performance and accuracy can be achieved by virtue of the growing and pruning mechanism. The OSFNNRG starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growth criteria as learning proceeds. In the parameter learning phase, all the free parameters of hidden units, regardless of whether they are newly created or originally existing, are updated by the extended Kalman filter (EKF) method. The performance of the OSFNNRG algorithm is compared with other popular approaches like OLS, RBF-AFS, DFNN and GDFNN in nonlinear dynamic system identification. Simulation results demonstrate that the learning speed of the proposed OSFNNRG algorithm is faster and the network structure is more compact with comparable generalization performance and accuracy.