Name
Abstract | ||
|---|---|---|
This paper proposes a novel approach for modelling complex interconnected systems by means of Mamdani fuzzy networks with feedforward rule bases. The nodes in these networks are rule bases connected in a feedforward manner whereby outputs from some rule bases are fed as inputs to subsequent rule bases. The approach allows any fuzzy network of this type to be presented as an equivalent Mamdani fuzzy system by linguistic composition of its nodes. The composition process makes use of formal models for fuzzy networks, basic operations in such networks, their properties and advanced operations. These models, operations and properties are used for defining several types of networks with single or multiple horizontal levels and vertical layers. The proposed approach facilitates the understanding of complex interconnected systems by improving the transparency of their models. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/IFS-151911 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Fuzzy modelling,decision support systems,finance,linguistic modelling,feedforward connections,complex systems | Complex system,Neuro-fuzzy,Fuzzy set operations,Computer science,Decision support system,Fuzzy logic,Fuzzy modelling,Artificial intelligence,Fuzzy control system,Machine learning,Feed forward | Journal |
Volume | Issue | ISSN |
30 | 5 | 1064-1246 |
Citations | PageRank | References |
1 | 0.35 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander E. Gegov | 1 | 47 | 8.47 |
| David Sanders | 2 | 9 | 6.25 |
| Boriana Vatchova | 3 | 9 | 3.20 |