Algorithms for better representation and faster learning in radial basis function networks
Advances in neural information processing systems 2
A resource-allocating network for function interpolation
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
A hyperrectangle-based method that creates RBF networks
Radial basis function networks 1
Fast learning in networks of locally-tuned processing units
Neural Computation
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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An improved radial basis function neural network (IRBFNN) with unsymmetrical Gaussian function is presented to simplify the structure of RBFNN. The improved resource allocating network (IRAN) is developed to design IRBFNN online for nonlinear dynamic system modeling, integrating the typical resource allocating network (RAN) with merging method for similar hidden units, deleting strategy for redundant hidden units, and LMS learning algorithm with moving data window for output link weights of network. The proposed approach can effectively improve the precision and generalization of IRBFNN. The combination of IRBFNN and IRAN is competent for the online modeling of nonlinear dynamic systems. The feasibility and effectiveness of the modeling method have been demonstrated by simulations.