Cognitive Models for learning to control dynamic systems

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$100,000.00
Award Year:
2008
Program:
STTR
Phase:
Phase I
Contract:
FA9550-08-C-0044
Award Id:
85089
Agency Tracking Number:
F074-012-0118
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1953, 68th Street, Brooklyn, NY, 11204
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
190577200
Principal Investigator:
Zhong-Ping Jiang
Chief Scientist
(718) 260-3646
zjiang@control.poly.edu
Business Contact:
Xiaoming Zhuang
President
(917) 767-8142
xm_zhuang@yahoo.com
Research Institute:
POLYTECHNIC UNIV.
Zhong-Ping Jiang
Six Metrotech Center
Brooklyn, NY, 11201
(718) 260-3646
Nonprofit college or university
Abstract
The development of fast and robust learning models which can work in non-stationary environments or scenarios with rapidly changing goals is becoming a critical task in both military and civilian applications. The objective of this proposal is to develop new mathematical/computational models based on cognitive science principles that are capable of rapid learning for command and control problems. In Phase I, the focus is on the integration of current cognitive models, such as DFT, with practical methods in nonlinear systems and control theory. Stochastic resonance techniques will be applied for the first time to dynamic signal detection and dynamic decision-making tasks. The efficiency of the proposed cognitive learning algorithms will be tested and evaluated based on previously established experimental research with human decision tasks. Phase II will focus on the development of software for application of these novel learning models to some Air Force missions of critical importance. This phase involves both computer simulations and experimental validations with human decision tasks.

* information listed above is at the time of submission.

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