Abstract: This paper considers the approximation of stable continuous-time multivariable linear systems from a finite number of Markov parameters and second-order information indexes. It is shown that ...
Neural networks are universal function approximators, which means that given enough parameters, a neural net can approximate any multivariable continuous function to any desired level of accuracy. The ...
Abstract: A stochastic approximation algorithm is presented for on-line identification of linear, multivariable, discrete-time systems from noisy data without prior knowledge of the statistics of ...
Neurointervention is a highly specialized area of medicine and, as such, neurointerventional research studies are often more challenging to conduct, require large, multicenter efforts and longer study ...
ABSTRACT: Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs ...
ABSTRACT: Subsidence in a deformation area can be measured in various ways, examples being conventional high-precision leveling, differential InSAR and multi-temporal GPS surveys. Integration of ...
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