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Estudio comparativo de clasificadores empleados en el diagnóstico de fallos de sistemas industriales
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lazaro, Jose B. Prieto-Moreno, Alberto Llanes-Santiago, Orestes García-Moreno, E. Bertrand |
| Copyright Year | 2011 |
| Abstract | This paper, presents a comparative study of the performance of four classification techniques very used in fault diagnosis of industrial processes. The selected techniques were: k-Nearest neighbor (kNN), Partial least-squares (PLS), Artificial Neuronal Networks (ANN) and Support Vector Machines (SVM). The comparison is based in the classification capacity of the historical data and the generalization using new observations. The four techniques are applied to historical data of the known benchmark Tennessee Eastman industrial process. The comparison permitted to prove as the generalization capacity of the classification techniques grow with the complexity of classifiers without to increase the computational effort in the fault diagnosis. |
| Starting Page | 87 |
| Ending Page | 98 |
| Page Count | 12 |
| File Format | PDF HTM / HTML |
| Volume Number | 14 |
| Alternate Webpage(s) | http://revistascientificas.cujae.edu.cu/.%5CRevistas%5CMecanica%5CVol-14%5C2-2011%5C01_2011_02_87_98.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
