KIM L BLACKWELL
Veterinary in Silver Spring, MD
License number
Pennsylvania BV006064L
Category
Veterinary Medicine
Type
Veterinarian
Address
Address
2
Silver Spring, MD 20902
Pennsylvania
Pennsylvania
Personal information
See more information about KIM L BLACKWELL at radaris.comName
Address
Phone
9327 Tulsemere Rd, Randallstown, MD 21133
12003 Lafayette Ct, Silver Spring, MD 20902
16706 Livingston Rd, Accokeek, MD 20607
17106 Fitzroy Way, Olney, MD 20832
1003 Leeds Ave, Baltimore, MD 21229
Professional information
See more information about KIM L BLACKWELL at trustoria.com
Dynamically Stable Associative Learning Neural Network System
Dynamically Stable Associative Learning Neural Network System
US Patent:
5588091, Dec 24, 1996
Filed:
Mar 2, 1995
Appl. No.:
8/400124
Inventors:
Daniel L. Alkon - Bethesda MD
Thomas P. Vogl - Bethesda MD
Kim T. Blackwell - Wheaton MD
Garth S. Barbour - Laurel MD
Thomas P. Vogl - Bethesda MD
Kim T. Blackwell - Wheaton MD
Garth S. Barbour - Laurel MD
Assignee:
Environmental Research Institute of Michigan - Arlington VA
International Classification:
G06F 1518
US Classification:
395 24
Abstract:
A dynamically stable associative learning neural network system includes, in its basic architectural unit, at least one each of a conditioned signal input, an unconditioned signal input and an output. Interposed between input and output elements are "patches," or storage areas of dynamic interaction between conditioned and unconditioned signals which process information to achieve associative learning locally under rules designed for application-related goals of the system. Patches may be fixed or variable in size. Adjustments to a patch radius may be by "pruning" or "budding. " The neural network is taught by successive application of training sets of input signals to the input terminals until a dynamic equilibrium is reached. Enhancements and expansions of the basic unit result in multilayered (multi-subnetworked) systems having increased capabilities for complex pattern classification and feature recognition.
Dynamically Stable Associative Learning Neural Network System
Dynamically Stable Associative Learning Neural Network System
US Patent:
5822742, Oct 13, 1998
Filed:
Feb 24, 1995
Appl. No.:
8/331554
Inventors:
Daniel L. Alkon - Bethesda MD
Thomas P. Vogl - Bethesda MD
Kim T. Blackwell - Wheaton MD
Garth S. Barbour - Laurel MD
Thomas P. Vogl - Bethesda MD
Kim T. Blackwell - Wheaton MD
Garth S. Barbour - Laurel MD
Assignee:
The United States of America as represented by the Secretary of Health &
Human Services - Washington DC
ERIM International, Inc. - Ann Arbor MI
Human Services - Washington DC
ERIM International, Inc. - Ann Arbor MI
International Classification:
G06F 1518
US Classification:
706 31
Abstract:
A dynamically stable associative learning neural system includes a plurality of neural network architectural units. A neural network architectural unit has as input both condition stimuli and unconditioned stimulus, an output neuron for accepting the input, and patch elements interposed between each input and the output neuron. The patches in the architectural unit can be modified and added. A neural network can be formed from a single unit, a layer of units, or multiple layers of units.
Top Down Preprocessor For A Machine Vision System
Top Down Preprocessor For A Machine Vision System
US Patent:
5870493, Feb 9, 1999
Filed:
Mar 17, 1997
Appl. No.:
8/819142
Inventors:
Thomas P. Vogl - Bethesda MD
Kim T. Blackwell - Wheaton MD
Daniel L. Alkon - Bethesda MD
Kim T. Blackwell - Wheaton MD
Daniel L. Alkon - Bethesda MD
Assignee:
The United States of America as represented by the Department of Health
and Human Services - Washington DC
ERIM, International, Inc. - Ann Arbor MI
and Human Services - Washington DC
ERIM, International, Inc. - Ann Arbor MI
International Classification:
G06K 936, G06K 946
US Classification:
382195
Abstract:
An image recognition and classification system includes a preprocessor in which a "top-down" method is used to extract features from an image; an associative learning neural network system, which groups the features into patterns and classifies the patterns: and a feedback mechanism which improves system performance by tuning preprocessor scale, feature detection, and feature selection.