| Document Date: 2008-01-23 11:12:59
Open Document File Size: 244,40 KBShare Result on Facebook
City London / Boston / Baltimore / Englewood CLiffs / / Company PWS Publishing Co. / Prentice-Hall / / Currency pence / / / Facility Prentice Hall / / IndustryTerm real-time system / unknown network / linearized least squares solution / neural network / matrix product / recursive linearized least squares algorithm / multi-layer neural networks / nonlinear multi-layer neural networks / on-line training methods / real-time training methods / adaptive systems / gradient descent algorithm / neural networks / nonlinear neural networks / poorer solution / radial basis function neural networks / typical neural network / real-time operations / nonlinear network / adaptive network / on-line method / linear least-squares solution / steepest descent algorithm / / Organization Oklahoma State University / / Person Substituting Eq / Martin T. Hagan / John Hopkins / / / Position Prime Minister / training feed-forward / / ProvinceOrState New Jersey / / Technology RLS algorithm / steepest descent algorithm / ULLS algorithm / neural network / recursive linearized least squares algorithm / gradient descent algorithm / Simulation / /
SocialTag |
On-line Least-Squares Training For The Underdetermined Case Roger L. Schultz, [removed], Halliburton Energy Services Martin T. Hagan, [removed], Oklahoma State University Abstra