We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Bernard Widrow is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 1975 — 1977 |
Widrow, Bernard |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Pattern Analysis and Recognition by Means of "Rubber Masks" |
1 |
| 1978 — 1982 |
Widrow, Bernard |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Adaptive Inverse Model Control |
1 |
| 1992 — 1995 |
Widrow, Bernard |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Neural Networks For Adpative Nonliner Control
This award funds the first year of a three year continuing grant. A number of basic scientific issues remain to be addressed in order to facililtate the wide use of neural networks in control systems. This research focuses on trainable state estimation for neural networks in order to deal with effects of plant and sensor noise and incomplete availability of state measurments. It will also explore neural network implementation of a self-tuning regulator for adapting a controller to track changes in a nonlinear plant; techniques for controller weight initialization that can decrease network training time and also reduce the probability of convergence of the weights to undersirable local minima; and adaptation of networks for navigational obstacle avoidance to robotic manipulator obstacles avoidance.
|
1 |
| 1995 — 1998 |
Widrow, Bernard |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Punish/Reward: Learning-With-a-Critic in Neural Networks
9521673 Lewis This project will investigate investigate neural network, (NN), for control of nonlinear dynamical systems. Its objective is to provide a framework for repeatable design of stable NN controllers for large classes of nonlinear systems. It will use nonlinear stability theory (e.g. input -output stability and passivity notions introduced in the 60's by one of the PIs) to study approximation properties of NN, with emphasis on guaranteeing NN controller performance in terms of small tracking errors and bounded NN weights (implying bounded inputs). Re eatable design algorithms will be given for NN controllers. Without guaranteed for performance and sensible design algorithms, NN controller s will rightfully not be accepted bye the control system community or US industry. NN controllers have the potential to significantly improve manufacturing process control in the US, since they are model-free and do not require explicit dynamics of the plant. The UTA work will be done at the Automation and robotics Research Institute (ARRI), so that technology transfer to industry will occur. The NN controllers developed will be implemented on the Flexibly-Link Systems Testbed, and then installed on the Manufacturing Surface Finishing Station. The ARRI Surface Finishing Consortium has offered matching money to finance the extensive technical work needed for this industrial application. ARRI Manager J. M. Fitzgerald, PE, and Dr. Kai Liu will be assigned on a matching fund basis to supervise this project,
|
1 |
