Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems

✍ Scribed by R. Alcalá; J. Alcalá-Fdez; J. Casillas; O. Cordón; F. Herrera

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 569 KB
• Volume: 22
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.20232

✦ Synopsis

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi-Sugeno-Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL ~a methodology to obtain Genetic Fuzzy Rule-Based Systems under the Iterative Rule Learning approach! has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics-based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL ~taking as a base some initial linguistic fuzzy partitions!. Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching-based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real-world problems, achieving good results.