The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 31, 2007

Filed:

Mar. 03, 2004
Applicants:

Shigeru Fujii, Iwata, JP;

Hitoshi Watanabe, Iwata, JP;

Sergey A. Panfilov, Crema, IT;

Kazuki Takahashi, Crema, IT;

Sergey V. Ulyanov, Crema, IT;

Inventors:

Shigeru Fujii, Iwata, JP;

Hitoshi Watanabe, Iwata, JP;

Sergey A. Panfilov, Crema, IT;

Kazuki Takahashi, Crema, IT;

Sergey V. Ulyanov, Crema, IT;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05B 13/00 (2006.01); G05B 13/04 (2006.01); G05B 15/00 (2006.01);
U.S. Cl.
CPC ...
Abstract

A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a motorcycle is described. In one embodiment, a simulation model of the motorcycle and rider control is used. In one embodiment, the simulation model includes a feedforward rider model. 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; 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.


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