Rapid category learning and recognition system

• US 5,157,738 A
• Filed: 12/18/1990
• Issued: 10/20/1992
• Est. Priority Date: 12/18/1990
• Status: Expired due to Term

• Current Assignee: Trustees of Boston University
• Original Assignee: Trustees of Boston University
• Inventors: Rosen, David B., Grossberg, Stephen, Carpenter, Gail A.
• Primary Examiner(s): Moore, David K.
• Assistant Examiner(s): Cammarata, Michael
• Application Number:
  US07/629,393
• Time in Patent Office: 672 Days
• Field of Search: 382/14, 382/15, 382/37, 382/38, 382/49, 382/10, 364/274.9, 364/276.6, 364/513, 364/972.4, 395/20-27
• US Class Current: 382/157
• CPC Class Codes: G06F 18/23211 with adaptive number of clusters
• CPC Class Codes: G06N 3/0409 Adaptive resonance theory [ART] networks

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.

Claims

1. A pattern recognition device, comprising:

a short term memory input field for presenting input signals defining an input pattern, the input pattern having certain properties;

a long term memory category representation field comprising plural category nodes, each such node (i) providing template signals defining a corresponding long term memory template, and (ii) having an indication of state of the node including commitment and rejection states of the node;

selector means for selecting at least one category node in the long term memory field as a function of at least the input pattern from the short term memory field; and

adjustment means responsive to each input pattern, for adjusting commitment and rejection states of category nodes and for adapting the corresponding long term memory template of the selected node to the input pattern, such that (i) upon the selector means selecting an uncommitted category node, the adjustment means adapts the corresponding long term memory template to match the input pattern, and (ii) upon the selector means selecting a committed category node, the adjustment means adapts the corresponding long term memory template to comprise a portion of a previous long term memory template of the committed category node and a complementary portion of the input pattern, said adapting by the adjustment means including (a) comparing template signals to a predetermined threshold, and (b) for each template signal falling below the threshold, permanently setting the template signal to zero.

2. A pattern recognition device as claimed in claim 1 wherein in response to selector means selection of an uncommitted category node, the adjustment means adapts the corresponding long term memory template to match the input pattern by the end of input pattern presentation time in the short term memory field.

3. A pattern recognition device as claimed in claim 1 wherein in response to selector means selection of a committed category node, the adjustment means adapts the corresponding long term memory template to approach the input pattern in a single computational step.

4. A pattern recognition device as claimed in claim 1 wherein the predetermined threshold is in the range 0 to 1 inclusive.

5. A pattern recognition device as claimed in claim 1 wherein:

the selector means selects category nodes by weighted signals of the input pattern; and

the selector means further comprises a reset member such that in response to selector selection of a committed category node by a weighted signal less than threshold ρ

*, the reset member resets category selection to an uncommitted category node, said threshold being predefined as 0≦

ρ

*≦

1.

6. A pattern recognition device as claimed in claim 1 wherein for each adaptation, the adjustment means adapts the corresponding long term memory template proportionally to the input pattern.

7. A pattern recognition device, comprising:

a short term memory input field for providing input signals defining an input pattern;

a long term memory field comprising plural category nodes, each such note (i) providing template signals defining a corresponding long term memory template and (ii) having an indication of state of the node, including commitment and rejection states thereof;

selector means for selecting at least one category node in the long term memory field based on the input pattern from the short term memory field; and

adjustment means for adjusting commitment and rejection states of category nodes and for adapting the corresponding long term memory template of the selected node to the input pattern in response to the input pattern such that

i) selector means selection of a previously uncommitted category node results in adjustment means adapting the corresponding long term memory template to immediately match the input pattern, and

ii) selector means selection of a previously committed category node results in adjustment means adapting the corresponding long term memory template to comprise complementary portions of the previous long term template of the selected category node and the input pattern.

8. A pattern recognition device as claimed in claim 7 wherein the adjustment means adapts the corresponding long term template to the input pattern at a nearly exponential rate.

9. A pattern recognition as claimed in claim 7 wherein the adjustment means adapts the corresponding long term memory template to the input pattern by comparing template signals to a predetermined threshold, such that for a template signal below the threshold, the adjustment means permanently sets the template signal to zero.

10. In a pattern recognition device having a) a short term memory field for providing input signals defining an input pattern, b) a long term memory field comprised of category nodes, each such node providing template signals defining a long term memory template, and c) a selector for selecting at least one category node in the long term memory field based on an input pattern from the short term memory field, template signals of the selected node generating a corresponding long term memory template, a method of adapting the corresponding template to the input pattern, said method comprising the steps of:

comparing template signals to a predefined threshold;

for each template signal below the threshold permanently setting the template signal to zero;

when the selector selects an uncommitted category node, adapting the corresponding template to match the input pattern; and

when the selector selects a committed category node, adapting the corresponding template to comprise a portion of a previous long term memory template of the committed category node and a complementary portion of the input pattern.

11. A method as claimed in claim 10 further comprising the steps of:

providing an indication of commitment and rejection states of each category node;

adjusting commitment and rejection states of category nodes in response to an input pattern;

in response to selector selection of a previously uncommitted category node, adapting the corresponding long term memory template to immediately match the input pattern; and

in response to selector selection of a previously committed category node, adapting the corresponding long term memory template to include a combination of a portion of the previous long term template of the selected category node and a complementary portion of the input pattern.

12. A method as claimed in claim 11 further comprising the step of resetting selection of a committed category node to an uncommitted category node where a weighted signal of category selection is less than threshold ρ

*, where ρ

* is predefined in the range 0 to 1 inclusive.

13. A method as claimed in claim 10 further comprising the step of adaptation the corresponding long term memory template proportionally to the input pattern for each adaption thereto.

14. A pattern recognition device, comprising:

a short term memory input field for presenting input signals defining an input pattern, the input pattern having certain properties;

a long term memory category representation field comprising plural category nodes, each such node (i) providing template signals defining a long term memory template, and (ii) having an indication of state of the node including commitment and rejection states of the node;

selector means for selecting at least one category node in the long term memory field by weighted signals of the input pattern, said selector means comprising a reset member such that, in response to selector selection of a committed category node by a weighted signal less than threshold ρ

*, the reset member resets category selection to an uncommitted category node, said threshold being predefined as 0≦

ρ

*≦

1; and

adjustment means responsive to each input pattern, for adjusting commitment and rejection states of category nodes and for adapting the corresponding long term memory template of the selected node to the input pattern, said adapting by the adjustment means including (a) comparing template signals to a predetermined threshold, and (b) for each template signal falling below the threshold, permanently setting the template signal to zero.

15. In a pattern recognition device having a) a short term memory field for providing input signals defining an input pattern, b) a long term memory field comprised of category nodes, each such node providing template signals defining a long term memory template, and c) a selector for selecting at least one category node in the long term memory field based on an input pattern from the short term memory field, template signals of the selected node generating a corresponding long term memory template, a method of adapting the corresponding template to the input pattern comprising the steps of:

providing an indication of commitment and rejection states of each category node;

adjusting commitment and rejection states of category nodes in response to an input pattern;

in response to selector selection of a previously uncommitted category node, adapting the corresponding long term memory template to immediately match the input pattern;

in response to selector selection of a previously committed category node, adapting the corresponding long term memory template to include a combination of a portion of the previous long term template of the selected category node and a complementary portion of the input pattern;

comparing template signals to a predefined threshold; and

for each template signal below the threshold permanently setting the template signal to zero.