Abstract
This paper presents an evolutionary algorithm for generating knowledge bases for fuzzy logic systems. The algorithm dynamically adjusts the focus of the genetic search by dividing the population into three sub-groups, each concerned with a different level of knowledge base optimisation. The algorithm was tested on the identification of two highly non-linear simulated plants. Such a task represents a challenging test for any learning technique and involves two opposite requirements, the exploration of a large high-dimensional search space and the achievement of the best modelling accuracy. The algorithm achieved learning results that compared favourably with those for alternative knowledge base generation methods.
Original language | English |
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Pages (from-to) | 583-592 |
Number of pages | 10 |
Journal | Engineering Applications of Artificial Intelligence |
Volume | 19 |
Issue number | 6 |
DOIs | |
Publication status | Published - Sept 2006 |
Keywords
- Evolutionary algorithms
- Fuzzy logic
- Systems modeling
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering