Guidance system

• US 20060224543A1
• Filed: 04/05/2005
• Published: 10/05/2006
• Est. Priority Date: 04/05/2005
• Status: Abandoned Application

• Current Assignee: John Richard Clymer, Lary Don Smith
• Original Assignee: John Richard Clymer, Lary Don Smith
• Inventors: Clymer, John Richard, Smith, Lary Don
• Application Number:
  US11/099,785
• Publication Number:
  US 20060224543A1
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06F 18/24765 Rule-based classification
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/025 Extracting rules from data

Abstract

A Feature Map and learning rule-based Classifier capability is demonstrated that maximizes performance of classifier decisions. The invention discriminates among a large set of cases to make the correct decision for each case and generalizes well by correctly deciding cases never seen before. The Feature Map capability consists of Kohonen Self Organizing Feature Map (SOFM) followed by a four layer Evolutionary Perceptron. During training, the SOFM learns a set exemplars, one for each case. In operation, the SOFM takes an input state vector and maps it into the closest exemplar. The Evolutionary Perceptron uses algorithms that evolve both the NN weights and sigma (step size) and has two hidden layers required to discriminate nonlinear class boundaries. A population of NNs is evolved from generation to generation by modifying weights and sigma values. The evolutionary programming approach in the classifier capabiliy is used to search through from most general to most specific rules.

Claims

1. A rules-based system for effecting computer implemented decision support, the system comprising:

1) a Feature Map comprising information relevant to a specified problem;

2) a learning Classifier capable of selecting and ordering rules for generating solutions to the specified problem;

3) means for dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions; and

,

4) means for outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.

2. A system according to claim 1, wherein said Feature Map comprises a self-organizing feature map.

3. A system according to claim 1, wherein said Feature Map further comprises an evolutionary perceptron.

4. A system according to claim 3, wherein said evolutionary perceptron comprises evolutionary algorithms that evolve populations of output features sets that are inputs to the learning classifies

5. A system according to claim 4, wherein said Feature Map further comprises a set of exemplars, which said exemplars are provided as initial or starting values.

6. A system according to claim 5, wherein said Feature Map further comprises the ability to receive input data and map it into the closest said exemplar.

7. A system according to claim 1, wherein said Classifier further comprises evolutionary optimization techniques.

8. A system according to claim 1, wherein said Classifier further comprises evolutionary programming that allows said Classifier to search through the set of all possible rules rule space from most general to most specific.

9. A system according to claim 1, wherein said Classifier further comprises the ability to evaluate the best-fitting rules and produce a decision.

10. A system according to claim 1, wherein said system is capable of classifying input cases into a predetermined number of sets.

11. A system according to claim 1, wherein said system is capable of deriving solutions that achieve predetermined system goals.

12. A system according to claim 1, wherein said system is capable of transmitting solutions to output devices and to thereby control the operation of said output devices.

13. A system according to claim 1, wherein said system is capable of controlling the operation of a traffic signal light.

14. A system according to claim 1, wherein said system is capable of reducing the number of state vector dimensions to a level acceptable for rule learning.

15. A system according to claim 1, wherein said system is capable of providing targeting solutions to an output targeting apparatus.

16. A system according to claim 1, further comprising a computer program to operate said system on a computer.

17. A system according to claim 1, wherein said system can be trained ahead of use using a training set of cases.

18. A system according to claim 1, wherein said system can be trained from a set of training cases developed by a simulation model.

24. A method according to claim 3, wherein said evolutionary perceptron comprises evolutionary algorithms that evolve populations of new feature sets.

25. A method according to claim 4, wherein said Feature Map further comprises a set of exemplars, which said exemplars are provided as initial or starting values.

26. A method according to claim 5, wherein said Feature Map further comprises the ability to receive input data and map it into the closest said exemplar.

19. A computer program readable by a machine, tangibly embodying a program of instructions executable by a machine to perform system steps for effecting computer implemented decision support, the system comprising the steps of:

1) collecting within a Feature Map information relevant to a specified problem;

2) selecting and ordering rules for generating solutions to the specified problem, via a learning Classifier;

3) dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions; and

,

4) outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.

20. A computer program readable by a machine, tangibly embodying a program of instructions executable by a machine to perform system steps for effecting computer implemented decision support, the system comprising the steps of:

1) collecting within a Feature Map information relevant to a specified problem;

2) selecting and ordering rules for generating solutions to the specified problem, via a learning Classifier;

3) dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions;

4) evolutionarily iterating said solutions until a final decisive solution is reached; and

,

5) outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.

21. A computer implemented rules-based method for effecting decision support, the method comprising:

1) constructing a Feature Map comprising information relevant to a specified problem;

2) utilizing a learning Classifier to select and order rules for generating solutions to the specified problem, based on input from said Feature Map;

3) dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions; and

,

4) outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.

22. A method according to claim 21, wherein said Feature Map comprises a self-organizing feature map.

23. A method according to claim 21, wherein said Feature Map further comprises an evolutionary perceptron.

27. A method according to claim 21, wherein said Classifier further comprises evolutionary optimization techniques.

28. A method according to claim 21, wherein said Classifier further comprises evolutionary programming that allows said Classifier to search through the set of all possible rules (rule space) from most general to most specific.

29. A method according to claim 21, wherein said Classifier further comprises the ability to evaluate the best-fitting rules and produce a decision.

30. A method according to claim 21, wherein said method is capable of classifying input cases into a predetermined number of sets.

31. A method according to claim 21, wherein said method is capable of deriving solutions that achieve predetermined system goals.

32. A method according to claim 21, wherein said method is capable of transmitting solutions to output devices and to thereby control the operation of said output devices.

33. A method according to claim 21, wherein said method is capable of controlling the operation of a traffic signal light.

34. A method according to claim 21, wherein said method is capable of reducing the number of state vector dimensions to a level acceptable for rule learning.

35. A method according to claim 21, wherein said method is capable of providing targeting solutions to an output targeting apparatus.

36. A method according to claim 21, further comprising a computer program to operate said method on a computer.

37. A method according to claim 21, wherein said method can be trained ahead of use using a training set of cases.

38. A method according to claim 21, wherein said method can be trained from a set of training cases developed by a simulation model.

39. A method employing a computer program readable by a machine, tangibly embodying a program of instructions executable by a machine to perform system steps for effecting computer implemented decision support, the method comprising the steps of:

1) collecting within a Feature Map information relevant to a specified problem;

2) selecting and ordering rules for generating solutions to the specified problem, via a learning Classifier;

3) dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions; and

,

4) outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.

40. A method employing a computer program readable by a machine, tangibly embodying a program of instructions executable by a machine to perform system steps for effecting computer implemented decision support, the method comprising the steps of:

1) collecting within a Feature Map information relevant to a specified problem;

2) selecting and ordering rules for generating solutions to the specified problem, via a learning Classifier;

3) dynamically interacting with the at least one of selectable candidate solutions for generating a refined set of candidate solutions;

4) evolutionarily iterating said solutions until a final decisive solution is reached; and

,

5) outputting or communicating the at least one selectable candidate solution to an appropriate displaying device or agent requiring solutions input.