US 2006/0218108 A1 · System for soft computing simulation

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.

Claims20

Dependent claims

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.

4 more elements

Dependencies are inferred from the claim text.

System for soft computing simulation

Life of this patent

System for soft computing simulation

Life of this patent

Abstract

Claims20