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US 5,214,715 AGranted
Predictive self-organizing neural network
AssigneeTrustees of Boston University
InventorsGail A. Carpenter, Stephen Grossberg, John W. Reynolds
Life of this patent
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Abstract
An A pattern recognition subsystem responds to an A feature representation input to select A-category-representation and predict a B-category-representation and its associated B feature representation input. During learning trials, a predicted B-category-representation is compared to that obtained through a B pattern recognition subsystem. With mismatch, a vigilance parameter of the A-pattern-recognition subsystem is increased to cause reset of the first-category-representation selection. Inputs to the pattern recognition subsystems may be preprocessed to complement code the inputs.
Claims26
A pattern recognition system comprising:
- an A pattern recognition subsystem for searching, selecting and learning an A-category-representation in response to an A input pattern;
- means for predicting a B-category-representation from a selected A-category-representation;
- means for providing a control B-category-representation;
- means for detecting a mismatch between a predicted B-category-representation and a control B-category representation;
- and
- means responsive to a mismatch between a predicted B-category-representation and a control B-category-representation to cause selection of a new A-category-representation.
3 more elements
A pattern recognition system comprising:
- A. an A pattern recognition subsystem comprising:
- an A feature representation field of nodes for receiving A input signals, defining an A input pattern, and A template signals;
- means for selecting an A-category-representation in an A-category-representation field of nodes based on a pattern from the feature representation field;
- means for generating the A template signals based on the selected A-category-representation;
- means for adapting A-category-representation selection and the A template signals to the A input signals;
- and
- A reset means for resetting A-category-representation selection with an insufficient match between the A input pattern and the A template signals at a first level of matching vigilance;
- B. a B pattern recognition subsystem comprising:
- a B feature representation field of nodes for receiving B input signals, defining a B input pattern, and B template signals;
- means for selecting a B-category-representation in a B-category-representation field of nodes based on a pattern from the B feature representation field;
- means for generating the B template signals based on the selected B-category-representation;
- means for adapting B-category-representation selection with an insufficient match between the B input pattern and the B template signals;
- C. means for associating the selected A-category-representation with a predicted B-category-representation comprising a mapping field of nodes having a one-to-one correspondence with nodes of the B-category-representation field and adaptive mapping from nodes in the A-category-representation field, the predicated B-category-representation associated with a selected first-category-representation being learned as the B-category-representation selected by the B pattern recognition subsystem;
- and
- D. system reset means for resetting an A-category-representation selection in the A pattern recognition subsystem where a predicted B-category-representation associated with the selected A-category-representation fails to match a B-category-representation selected by the B pattern recognition subsystem, the system reset means increasing vigilance of the A reset means to cause reset of the A-category-representation selection, increased vigilance being maintained with subsequent selection of an A-category-representation with said A input signals applied to the A feature representation field.
12 more elements
A pattern recognition method comprising:
- searching, selecting and learning an A-category-representation in response to an A input pattern;
- predicting a B-category-representation from a selected A-category-representation;
- providing a control B-category-representation;
- detecting a mismatch between the predicted B-category-representation and the control B-category-representation;
- and
- selecting a new A-category-representation when a mismatch between the predicted B-category-representation and the control B-category-representation is detected.
3 more elements
Dependencies are inferred from the claim text.