Comparison of neural network classifiers for NSCAT sea ice flag

Park, Jun-Dong; Jones, W. Linwood

doi:10.1109/igarss.1998.703798

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Radial Basis Functions Neural Network1

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Cited by 4 publications

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“…In these networks the learning involves only one layer with lesser computations. This results in reduction in the training time in comparison with multi layered feedforward neural networks (MFNN), that use back propagation algorithm to update the weights of all the layers [18], [19]. These features make RBFNN attractive in many practical problems.…”

Section: Radial Basis Functions Neural Networkmentioning

confidence: 99%

U-model Based Adaptive Tracking Scheme for Unknown MIMO Bilinear Systems

Azhar

AL-Sunni

Shafiq

2006

2006 1ST IEEE Conference on Industrial Electronics and Applications

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Bilinear systems are attractive candidates for many dynamical processes, since they allow a significantly larger class of behaviour than linear systems, yet retain a rich theory which is closely related to the familiar theory of linear systems. A new technique for the control of unknown MIMO bilinear systems is introduced. The scheme is based on the U-model with identification based on Radial Basis Functions neural networks which is known for mapping any nonlinear function. U-Model is a control oriented model used to represent a wide range of non-linear discrete time dynamic plants. The proposed tracking scheme is presented and verified using simulation examples. 0-7803-9514

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Section: Radial Basis Functions Neural Networkmentioning

confidence: 99%