Name
Title | ||
|---|---|---|
Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm |
Abstract | ||
|---|---|---|
This paper proposes a novel method for training the parameters of an adaptive network based fuzzy inference system (ANFIS). Different from previous approaches, which emphasized on the use of gradient descent (GD) methods, we employ a method based on. Quantum-behaved Particle Swarm Optimization (QPSO) for training the parameters of an ANFIS. The ANFIS trained by the proposed method is applied to nonlinear system modeling and chaotic prediction. The simulation results show that the ANFIS-QPSO method performs much better than the original ANFIS and the ANFIS-PSO method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-30976-2_18 | ICSI (1) |
Keywords | Field | DocType |
system modeling,chaotic prediction,novel method,fuzzy inference system,adaptive network,anfis-qpso method,quantum-behaved particle swarm optimization,anfis-pso method,training anfis parameter,original anfis,particle swarm optimization | Nonlinear system,Computer science,Artificial intelligence,Adaptive neuro fuzzy inference system,Chaotic,Particle swarm optimization,Quantum,Gradient descent,Mathematical optimization,Algorithm,Multi-swarm optimization,Machine learning,Fuzzy inference system | Conference |
Volume | ISSN | Citations |
7331 | 0302-9743 | 4 |
PageRank | References | Authors |
0.38 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiufang Lin | 1 | 4 | 0.72 |
| Jun Sun | 2 | 1060 | 79.09 |
| Vasile Palade | 3 | 1353 | 114.44 |
| Wei Fang | 4 | 339 | 19.89 |
| Xiaojun Wu | 5 | 230 | 11.79 |
| Wenbo Xu | 6 | 120 | 4.77 |