Abstract
With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of fuzzy logic systems. Current fuzzy logic systems are developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. In this chapter a method for development of fuzzy systems that can interact with other (fuzzy) systems is proposed. Specifically a method for designing hierarchical self-learning fuzzy logic control systems based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control of mobile robots for collision-avoidance in a common workspace. The robots are considered as point masses moving in a common work space. Genetic algorithms are employed as an adaptive method for learning the fuzzy rules of the control systems as well as learning, the mapping and interaction between fuzzy knowledge bases of different fuzzy logic systems.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Information Technologies |
| Subtitle of host publication | Concepts, Methodologies, Tools, and Applications: Volume I-IV |
| Publisher | IGI Global |
| Pages | 1204-1214 |
| Number of pages | 11 |
| Volume | 1-4 |
| ISBN (Electronic) | 9781599049427 |
| ISBN (Print) | 1599049414, 9781599049410 |
| DOIs | |
| Publication status | Published - 1 Jan 2007 |