Soft computing optimizer of intelligent control system structures

• US 7,219,087 B2
• Filed: 07/23/2004
• Issued: 05/15/2007
• Est. Priority Date: 07/25/2003
• Status: Expired due to Fees

• Current Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Original Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Inventors: Takahashi, Kazuki, Panfilov, Sergey A., Ulyanov, Sergey V., Litvintseva, Ludmila, Ulyanov, Viktor S.
• Primary Examiner(s): Knight; Anthony
• Assistant Examiner(s): Kennedy; Adrian L.
• Application Number:
  US10/897,978
• Publication Number:
  US 20050119986A1
• Time in Patent Office: 1,026 Days
• Field of Search: 706/59, 706/8, 706/50, 706/1, 706/23, 706/19, 700/28, 700/50
• US Class Current: 706/23
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/048 Fuzzy inferencing

Abstract

The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching 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, Tsukamoto, 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. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

Claims

1. A method for optimizing a knowledge base in a soft computing controller, comprising:

selecting a fuzzy model by selecting one or more parameters, said one or more parameters comprising at least one of a number of input variables, a number of output variables, a type of fuzzy inference model, and a teaching signal;

optimizing linguistic variable parameters of a knowledge base according to said one or more parameters to produce optimized linguistic variables;

ranking rules in said rule base according to firing strength;

eliminating rules with relatively weak firing strength leaving selected rules from said rules in said rule base; and

optimizing said selected rules, using said fuzzy model, said linguistic variable parameters and said optimized linguistic variables, to produce optimized selected rules.

2. The method of claim 1, further comprising optimizing said selected rules using a derivative-based optimization procedure.

3. The method of claim 1, further comprising optimizing parameters of membership functions of said optimized selected rules to reduce approximation errors.