Soft computing optimizer of intelligent control system structures
US7219087B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 23, 2004 |
| Grant date | May 15, 2007 |
| Priority date | — |
| Expiry date | May 18, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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 …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.