Name
Abstract | ||
|---|---|---|
At present, several artificial intelligence (AI) techniques are used to identify complex systems. The data collected is extremely important, as it enables the evaluation, prediction and correction variables' behavior in any given process. The most recent methods tend to associate such techniques in order to obtain models that are continuously closer to those desired. This paper presents a method based on polynomial neural networks and fuzzy logics, optimized by a technique known as particle swarm optimization. The idea consists in generating a final structure that is compact, flexible and capable of producing good results when applied to resolving system identification problems and time series forecasting. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICSMC.2010.5642497 | Systems Man and Cybernetics |
Keywords | Field | DocType |
fuzzy logic,large-scale systems,load forecasting,neural nets,particle swarm optimisation,polynomials,power engineering computing,time series,PSO fuzzy polynomial neural network,artificial intelligence techniques,complex systems,fuzzy logics,particle swarm optimization,short term load forecasting,system identification problems,time series forecasting | Complex system,Particle swarm optimization,Time series,Proposition,Polynomial,Computer science,Fuzzy logic,Artificial intelligence,Artificial neural network,System identification,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-4244-6586-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yvo Marcelo C. Masselli | 1 | 0 | 0.34 |
| Germano Lambert-torres | 2 | 59 | 19.17 |
| Carlos Henrique Valério De Moraes | 3 | 1 | 1.16 |
| Carlos Henrique Valério De Moraes | 4 | 1 | 1.16 |
| Luiz Eduardo Borges da Silva | 5 | 32 | 6.80 |
| Ahmed Ali Abdalla Esmin | 6 | 33 | 3.82 |
| Lambert-Torres, G. | 7 | 0 | 0.68 |
| Borges da Silva, L.E. | 8 | 0 | 0.34 |