| dc.contributor.author |
Ferentinos, K |
en |
| dc.contributor.author |
Albright, L |
en |
| dc.contributor.author |
Selman, B |
en |
| dc.date.accessioned |
2014-06-06T06:45:21Z |
|
| dc.date.available |
2014-06-06T06:45:21Z |
|
| dc.date.issued |
2003 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/S0168-1699(03)00012-7 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/2388 |
|
| dc.subject |
backpropagation algorithm |
en |
| dc.subject |
Fault Detection |
en |
| dc.subject |
Generalization Capability |
en |
| dc.subject |
Limiting Factor |
en |
| dc.subject |
Nutrient Solution |
en |
| dc.subject |
Feedforward Neural Network |
en |
| dc.subject |
Most Probable Explanation |
en |
| dc.subject |
Neural Network |
en |
| dc.title |
Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/S0168-1699(03)00012-7 |
en |
| heal.publicationDate |
2003 |
en |
| heal.abstract |
In this work, two separate fault detection models are developed: one for the detection of faulty operation of a deep-trough hydroponic system which is caused by mechanical, actuator or sensor faults, and one for the detection of a category of biological faults (i.e. specific stressed situations of the plants), namely the “transpiration fault”. The neural network methodology was proved to |
en |
| heal.journalName |
Computers and Electronics in Agriculture |
en |
| dc.identifier.doi |
10.1016/S0168-1699(03)00012-7 |
en |