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Body mass index (BMI) was calculated from height self-reported at baseline and weight updated every 2 years.
Body mass index (kg/m) was calculated from self-reported body weight (updated biennially) and height (reported at baseline in 1986).
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Adaptive control with two different weight updating algorithms is discussed.
In addition, a truncated weight updating rule was used similar to Choy and Siu (IEEE Trans . Communications46 (3) (1998) 301 304).
This weight updating is called training, and large amounts of data, often labeled with the "right" answer, is used.
A novel approach is proposed for the weight updating mechanism based on the modification of the backpropagation algorithm.
An automatic weight updating scheme is developed to present the images on which it is best for the user to provide feedback.
The existence of the control signal is always assured by introducing a method of parameter hopping, which is incorporated in the weight updating law.
A new recursive formula for weight updating is derived from the optimization of certain object functions, which is an extension of the normalized LMS algorithm.
By employing a stabilizing term in the critic NN weight updating law, there is no requirement for adopting initial admissible control in the proposed algorithm.
Due to its linear nature and local weight updating, the BSN controller is more suitable for real-time implementation than conventional multi-layer feedforward neural controllers.
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