Abstract: In a pattern recognition system, input signals are applied to a short term feature representation field of nodes. A pattern from the short term feature representation field selects at least one category node in a category representation field. The selected category then generates a template pattern. With an insufficient match between the input pattern and template pattern, the category selection is reset. Category selection is based on selection weights which are initially set equal to long term memory weights. After reset, however, selections weights are reduced. Reduction is greatest at those nodes where excitation in F.sub.2 was greater prior to reset. The category representation field is of the same form as the field which receives the input and may itself serve as an input to a higher level pattern recognition system.

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.

Abstract: An improved ART2 network provides fast and intermediate learning. The network combines analog and binary coding functions. The analog portion encodes the recent past while the binary portion retains the distant past. LTM weights that fall below a threshold remain below threshold at all future times. The suprathreshold LTM weights track a time average of recent input patterns. LTM weight adjustment (update) provides fast commitment and slow recoding. The network incorporates these coding features while achieving an increase in computational efficiency of two to three orders of magnitude over prior analog ART systems.

Abstract: A self-categorizing pattern recognition system includes an adaptive filter for selecting a category in response to an input pattern. A template is then generated in response to the selected category and a coincident pattern indicating the intersection between the expected pattern and the input pattern is generated. The ratio between the number of elements and the coincident pattern to the number of elements in the input pattern determines whether the category is reset. If the category is not reset, the adaptive filter and template may be modified in response to the coincident pattern. Reset of the selected category is inhibited if no expected pattern is generated. Weighting of the adaptive filter in response to a coincident pattern is inversely related to the number of elements in the input pattern. The selected categories reset where a reset function is less than a vigilance parameter which may be varied in response to teaching events.

Abstract: A neural network includes a feature representation field which receives input patterns. Signals from the feature representative field select a category from a category representation field through a first adaptive filter. Based on the selected category, a template pattern is applied to the feature representation field, and a match between the template and the input is determined. If the angle between the template vector and a vector within the representation field is too great, the selected category is reset. Otherwise the category selection and template pattern are adapted to the input pattern as well as the previously stored template. A complex representation field includes signals normalized relative to signals across the field and feedback for pattern contrast enhancement.

Abstract: A neural network includes a feature representation field which receives input patterns. Signals from the feature representation field select a category from a category representation field through a first adaptive filter. Based on the selected category, a template pattern is applied to the feature representation field, and a match between the template and the input is determined. If the angle between the template vector and a vector within the representation field is too great, the selected category is reset. Otherwise the category selection and template pattern are adapted to the input pattern as well as the previously stored template. A complex representation field includes signals normalized relative to signals across the field and feedback for pattern contrast enhancement.