Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2007-05-15
2007-05-15
Knight, Anthony (Department: 2121)
Data processing: artificial intelligence
Neural network
Learning task
C706S001000, C706S019000, C700S028000, C700S050000
Reexamination Certificate
active
10897978
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.
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Litvintseva Ludmila
Panfilov Sergey A.
Takahashi Kazuki
Ulyanov Sergey V.
Ulyanov Viktor S.
Kennedy Adrian L.
Knight Anthony
Yamaha Hatsudoki Kabushiki Kaisha
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