System for soft computing simulation

• US 20060218108A1
• Filed: 10/04/2005
• Published: 09/28/2006
• Est. Priority Date: 03/24/2005
• Status: Abandoned Application

• Current Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Original Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Inventors: Ulyanov, Sergei, Panfilov, Sergey
• Application Number:
  US11/243,511
• Publication Number:
  US 20060218108A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G05B 13/0285 using neural networks and fuzzy logic
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

The present invention involves a Soft Computing Optimizer (SCOptimizer) for designing a Knowledge Base (KB) to be used in a control system for controlling a plant. The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and training signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal.

Claims

1. An optimizer, comprising:

a first dialog configured to allow a user to specify one or more linguistic variable parameters;

a second dialog configured to allow the user to specify one or more membership function types;

a first genetic optimizer configured to optimize said linguistic variable parameters for a fuzzy model in a fuzzy inference system;

a first knowledge base trained by a use of a training signal;

a rule evaluator configured to rank rules in said first knowledge base according to firing strength and eliminating rules with a relatively low firing strength to create a second knowledge base; and

a second genetic analyzer configured to optimize said second knowledge base using said fuzzy model.

2. The soft computing optimizer of claim 1, further comprising an optimizer configured to optimize said fuzzy inference model using classical derivative-based optimization.

3. The soft computing optimizer of claim 1, further comprising a third genetic optimizer configured to optimize a structure of said linguistic variables using said second knowledge base.

4. The soft computing optimizer of claim 1, further comprising a third genetic optimizer configured to optimize a structure of membership functions in said fuzzy inference system.

5. The soft computing optimizer of claim 1, wherein said second genetic analyzer uses a fitness function based on measured plant responses.

6. The soft computing optimizer of claim 1, wherein said second genetic analyzer uses a fitness function based on modeled plant responses.

7. The soft computing optimizer of claim 14, wherein said second genetic analyzer uses a fitness function configured to reduce entropy production of a controlled plant.

8. The soft computing optimizer of claim 1, wherein said first genetic algorithm is configured to choose a number of membership functions for said first knowledge base.

9. The soft computing optimizer of claim 1, wherein said first genetic algorithm is configured to choose a type of membership functions for said first knowledge base.

10. The soft computing optimizer of claim 1, wherein said first genetic algorithm is configured to choose parameters of membership functions for said first knowledge base.

11. The soft computing optimizer of claim 1, wherein a fitness function used in said second genetic algorithm depends, at least in part, on a type of membership functions in said fuzzy inference system.

12. The soft computing optimizer of claim 1, further comprising a third genetic analyzer configured to optimize said second knowledge base according to a search space from the parameters of said linguistic variables.

13. The soft computing optimizer of claim 1, further comprising a third genetic analyzer configured to optimize said second knowledge base by minimizing a fuzzy inference error.

14. The soft computing optimizer of claim 1, wherein said second genetic optimizer uses an information-based fitness function.

15. The soft computing optimizer of claim 1, wherein said first genetic optimizer uses a first fitness function and said second genetic optimizer uses said first fitness function.

18. The soft computing optimizer of claim 14, wherein said second genetic optimizer uses a fitness function configured to optimize based on user preferences.

19. The soft computing optimizer of claim 1, wherein said second genetic optimizer uses a nonlinear model of a controlled plant.

20. The soft computing optimizer of claim 1, wherein said second genetic optimizer uses a nonlinear model of an unstable plant.

21. The soft computing optimizer of claim 1, wherein said training signal is obtained from an optimal control signal.

22. The soft computing optimizer of claim 1, wherein said optimal control signal is computed using a plugin module.