Radial Basis Functions
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Kernel orthonormalization in radial basis function neural networks
IEEE Transactions on Neural Networks
Radial basis function networks and complexity regularization in function learning
IEEE Transactions on Neural Networks
Multiscale approximation with hierarchical radial basis functions networks
IEEE Transactions on Neural Networks
Generalized regression neural networks in time-varying environment
IEEE Transactions on Neural Networks
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
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In the paper we recover a Hammerstein system nonlinearity. Hammerstein systems, incorporating nonlinearity and dynamics, play an important role in various applications, and effective algorithms determining their characteristics are not only of theoretical but also of practical interest. The proposed algorithm is quasi-parametric, that is, there are several parametric model candidates and we assume that the target non-linearity belongs to the one of the classes represented by the models. The algorithm has two stages. In the first, the neural network is used to recursively filter (estimate) the nonlinearity from the noisy measurements. The network serves as a teacher/trainer for the model candidates, and the appropriate model is selected in a simple tournament-like routine. The main advantage of the algorithm over a traditional one stage approach (in which models are determined directly from measurements), is its small computational overhead (as computational complexity and memory occupation are both greatly reduced).