FAULT DETECTION AND IDENTIFICATION IN A DEEP TROUGH HYDROPONIC SYSTEM USING ADAPTIVE NEURO-FUZZY ANALYSIS
| dc.contributor.author | Setiawan, Albert | |
| dc.date.accessioned | 2008-02-13T15:16:28Z | |
| dc.date.available | 2013-02-13T07:22:55Z | |
| dc.date.issued | 2008-02-13T15:16:28Z | |
| dc.description.abstract | An early fault detection and identification system (FDI) can be an important part in any plant production system. A FDI can be used to avoid costly repairs and long disruptions in production. A hydroponic plant production system is a complex biological system that contains plants and microorganisms in its processes that are hard to model mathematically. A soft computing method called a neuro-fuzzy system is chosen to implement the FDI. A neuro-fuzzy system is a hybrid combination of a neural network and a fuzzy logic system that combines the best from both methods: knowledge based structure from fuzzy logic and a proven learning capability from a neural network. An adaptive neuro-fuzzy inference system (ANFIS) is developed to detect and identify actuator and sensor faults in the hydroponic plant production system. A separate system for exploring the ANFIS capability in detecting biological faults is also investigated. The novelty of the neuro-fuzzy FDI in this research used a single output to simultaneously detect and identify various faults in the system. | en_US |
| dc.identifier.other | bibid: 6397077 | |
| dc.identifier.uri | https://hdl.handle.net/1813/9958 | |
| dc.language.iso | en_US | en_US |
| dc.subject | neuro-fuzzy | en_US |
| dc.subject | fault detection | en_US |
| dc.subject | deep trough hydroponic | en_US |
| dc.subject | fault identification | en_US |
| dc.title | FAULT DETECTION AND IDENTIFICATION IN A DEEP TROUGH HYDROPONIC SYSTEM USING ADAPTIVE NEURO-FUZZY ANALYSIS | en_US |
| dc.type | dissertation or thesis | en_US |
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