Adaptive learning system and method

• US 20030149676A1
• Filed: 01/23/2003
• Published: 08/07/2003
• Est. Priority Date: 04/10/2000
• Status: Active Grant

• Current Assignee: Pacific Edge Biotechnology Limited
• Original Assignee: Pacific Edge Biotechnology Limited
• Inventors: Kasabov, Nikola Kirilov
• Granted Patent: US 7,089,217 B2
• Time in Patent Office: Days
• US Class Current: 706/2
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]

Abstract

The invention provides a neural network module comprising an input layer comprising one or more input nodes arranged to receive input data, a rule base layer comprising one or more rule nodes, an output layer comprising one or more output nodes, and an adaptive component arranged to aggregate selected two or more rule nodes in the rule base layer based on the input data. The invention also provides an adaptive learning system comprising one or more of the neural network modules of the invention. The invention further provides related methods of implementing a neural network module an adaptive learning system, and a neural network computer program.

Claims

1. A neural network module comprising:

an input layer comprising one or more input nodes arranged to receive input data;

a rule base layer comprising one or more rule nodes;

an output layer comprising one or more output nodes; and

an adaptive component arranged to aggregate selected two or more rule nodes in the rule base layer based on the input data.

2. A neural network module as claimed in claim 1 wherein each rule node in the rule base layer has a minimum activation threshold, each rule node arranged to be activated where input data satisfies the minimum activation threshold of the rule node.

3. A neural network module as claimed in claim 2 wherein the parameters of the activation threshold of each rule node activated by input data are adjusted based on the input data.

4. A neural network module as claimed in claim 2 or claim 3 wherein each rule node is assigned a magnitude of activation when activated by input data.

5. A neural network module as claimed in claim 4 wherein the adaptive component is arranged to aggregate two or more rule nodes based on the magnitude of activation when activated by input data.

6. A neural network module as claimed in any one of claims 2 to 5 wherein the adaptive component is arranged to increase the minimum activation threshold of one or more rule nodes not selected for aggregation.

7. A neural network module as claimed in any one of claims 2 to 6 wherein the parameters of the activation threshold of each rule node activated by input data are adjusted based on both the input data and desired output data.

8. A neural network module as claimed in any one of the preceding claims wherein the adaptive component is arranged to insert new rule nodes into the rule base layer.

9. A neural network module as claimed in any one of the preceding claims wherein the adaptive component is arranged to extract rules from the rule base layer.

10. A neural network module as claimed in any one of claims 3 to 9 wherein the parameters of the activation threshold of each rule node are adjusted based at least partially on new input data.

11. A neural network module as claimed in any one of claims 3 to 10 further comprising a memory in which is stored input data, wherein the parameters of the activation threshold of each rule node are adjusted based at least partially on the stored input data.

12. A neural network module as claimed in any one of the preceding claims further comprising a fuzzy input layer comprising one or more fuzzy input nodes arranged to transform input node values for use by the rule base layer.

13. A neural network module as claimed in any one of the preceding claims further comprising a fuzzy output layer comprising one or more fuzzy output nodes arranged to transform data output from the rule base layer.

14. An adaptive learning system comprising one or more neural network modules as claimed in any one of the preceding claims.

15. A method of implementing a neural network module comprising the steps of:

arranging an input layer comprising one or more input nodes to receive input data;

arranging a rule base layer comprising one or more rule nodes;

arranging an output layer comprising one or more output nodes; and

arranging an adaptive component to aggregate selected two or more rule nodes in the rule base layer based on the input data.

16. A method of implementing a neural network module as claimed in claim 15 further comprising the steps of assigning a minimum activation threshold to each rule node in the rule base layer;

and arranging each rule node to be activated where input data satisfies the minimum activation threshold of the rule node.

17. A method of implementing a neural network module as claimed in claim 16 further comprising the step of adjusting the parameters of the activation threshold of each rule node activated by input data based on the input data.

18. A method of implementing a neural network module as claimed in claim 16 or claim 17 further comprising the step of assigning to each rule node a magnitude of activation when activated by input data.

19. A method of implementing a neural network module as claimed in claim 18 further comprising the step of arranging the adaptive component to aggregate two or more rule nodes based on the magnitude of activation when activated by input data.

20. A method of implementing a neural network module as claimed in any one of claims 16 to 19 further comprising the step of arranging the adaptive component to increase the minimum activation threshold of one or more rule nodes not selected for aggregation.

21. A method of implementing a neural network module as claimed in any one of claims 16 to 20 further comprising the step of adjusting the parameters of the activation threshold of each rule node activated by input data based on both the input data and desired output data.

22. A method of implementing a neural network module as claimed in any one of claims 15 to 21 further comprising the step of arranging the adaptive component to insert new rule nodes into the rule base layer.

23. A method of implementing a neural network module as claimed in any one of claims 15 to 22 further comprising the step of arranging the adaptive component to extract rules from the rule base layer.

24. A method of implementing a neural network module as claimed in any one of claims 17 to 23 further comprising the step of adjusting the parameters of the activation threshold of each rule node based at least partially on new input data.

25. A method of implementing a neural network module as claimed in any one of claims 17 to 24 further comprising the steps of maintaining in a memory input data;

and adjusting the parameters of activation threshold of each rule node based at least partially on the stored input data.

26. A method of implementing a neural network module as claimed in any one of claims 15 to 25 further comprising the step of arranging a fuzzy input layer comprising one or more fuzzy input nodes to transform input node values for use by the rule base layer.

27. A method of implementing a neural network module as claimed in any one of claims 15 to 26 further comprising the steps of arranging a fuzzy output layer comprising one or more fuzzy output nodes to transform data output from the rule base layer.

28. A neural network computer program comprising:

an input layer comprising one or more input nodes arranged to receive input data;

a rule base layer comprising one or more rule nodes;

an output layer comprising one or more output nodes; and

an adaptive component arranged to aggregate selected two or more rule nodes in the rule base layer based on the input data.

29. A neural network computer program as claimed in claim 28 wherein each rule node in the rule base layer has a minimum activation threshold, each rule node arranged to be activated where input data satisfies the minimum activation threshold of the rule node.

30. A neural network computer program as claimed in claim 29 wherein the parameters of the activation threshold of each rule node activated by input data are adjusted based on the input data.

31. A neural network module as claimed in claim 29 or claim 30 wherein each rule node is assigned a magnitude of activation when activated by input data.

32. A neural network computer program as claimed in claim 31 wherein the adaptive component is arranged to aggregate two or more rule nodes based on the magnitude of activation when activated by input data.

33. A neural network computer program as claimed in any one of claims 29 to 32 wherein the adaptive component is arranged to increase the minimum activation threshold of one or more rule nodes not selected for aggregation.

34. A neural network computer program as claimed in any one of claims 29 to 33 wherein the parameters of the activation threshold of each rule node activated by input data are adjusted based on both the input data and desired output data.

35. A neural network computer program as claimed in any one of claims 28 to 34 wherein the adaptive component is arranged to insert new rule nodes into the rule base layer.

36. A neural network computer program as claimed in any one of claims 28 to 35 wherein the adaptive component is arranged to extract rules from the rule base layer.

37. A neural network computer program as claimed in any one of claims 30 to 36 wherein the parameters of activation threshold of each rule node are adjusted based at least partially on new input data.

38. A neural network computer program as claimed in any one of claims 30 to 37 further comprising input data stored in a memory, the parameters of the activation threshold of each rule node arranged to be adjusted based at least partially on the stored input data.

39. A neural network computer program as claimed in any one of claims 28 to 38 further comprising a fuzzy input layer comprising one or more fuzzy input nodes arranged to transform input node values for use by the rule base layer.

40. A neural network computer program as claimed in any one of claims 28 to 39 further comprising a fuzzy output layer comprising one or more fuzzy output nodes arranged to transform data output from the rule base layer.

41. An adaptive learning computer program comprising one or more neural network programs as claimed in any one of claims 28 to 40.

42. A neural network computer program as claimed in any one of claims 28 to 40 embodied on a computer-readable medium.

43. An adaptive learning computer program as claimed in claim 41 embodied on a computer-readable medium.