| dc.contributor.author |
Ferentinos, K |
en |
| dc.contributor.author |
Albright, L |
en |
| dc.date.accessioned |
2014-06-06T06:45:19Z |
|
| dc.date.available |
2014-06-06T06:45:19Z |
|
| dc.date.issued |
2003 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/S1537-5110(02)00232-5 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/2372 |
|
| dc.subject |
Combinatorial Problems |
en |
| dc.subject |
Fault Detection |
en |
| dc.subject |
Fault Detection and Diagnosis |
en |
| dc.subject |
Genetic Algorithm |
en |
| dc.subject |
Sensors and Actuators |
en |
| dc.subject |
System Development |
en |
| dc.subject |
Training Algorithm |
en |
| dc.subject |
Activation Function |
en |
| dc.subject |
Feedforward Neural Network |
en |
| dc.subject |
Neural Network |
en |
| dc.subject |
Neural Network Model |
en |
| dc.subject |
Real Time |
en |
| dc.title |
Fault Detection and Diagnosis in Deep-trough Hydroponics using Intelligent Computational Tools |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/S1537-5110(02)00232-5 |
en |
| heal.publicationDate |
2003 |
en |
| heal.abstract |
The intelligent computational tools of feedforward neural networks and genetic algorithms are used to develop a real-time detection and diagnosis system of specific mechanical, sensor and plant (biological) failures in a deep-trough hydroponic system. The capabilities of the system are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network |
en |
| heal.journalName |
Biosystems Engineering |
en |
| dc.identifier.doi |
10.1016/S1537-5110(02)00232-5 |
en |