Name
Abstract | ||
|---|---|---|
The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85920-8_53 | CIARP |
Keywords | Field | DocType |
selforganizing mechanism,anfis model,major concern,self-organizing neuro-fuzzy inference system,performance criterion,important application,optimal number,main purpose,neuro-fuzzy model,architectural design,classical anfis,self organization,satisfiability,anfis,neuro fuzzy | Architecture,Neuro-fuzzy,Architectural design,Computer science,Artificial intelligence,Adaptive neuro fuzzy inference system,Machine learning,Inference system | Conference |
Volume | ISSN | Citations |
5197 | 0302-9743 | 5 |
PageRank | References | Authors |
0.59 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Héctor Allende-cid | 1 | 22 | 12.60 |
| Alejandro Veloz | 2 | 18 | 4.42 |
| Rodrigo Salas | 3 | 36 | 6.52 |
| Steren Chabert | 4 | 8 | 2.07 |
| Héctor Allende | 5 | 148 | 31.69 |