東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Neural network fault detection and d...

Ferentinos, Konstantinos P.

FindBook

Google Book

Amazon

博客來

Neural network fault detection and diagnosis in deep-trough hydroponic systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural network fault detection and diagnosis in deep-trough hydroponic systems./
作者:	Ferentinos, Konstantinos P.
面頁冊數:	209 p.
附註:	Adviser: Louis D. Albright.
Contained By:	Dissertation Abstracts International62-12B.
標題:	Engineering, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3037245
ISBN:	0493502947

Neural network fault detection and diagnosis in deep-trough hydroponic systems.
Ferentinos, Konstantinos P.

Neural network fault detection and diagnosis in deep-trough hydroponic systems. - 209 p.

Adviser: Louis D. Albright.

Thesis (Ph.D.)--Cornell University, 2002.

A system for real-time detection and diagnosis of specific mechanical, sensor and plant (biological) failures is developed. The feedforward neural network methodology is used as the main tool for the development of the fault detection model and its capabilities are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network designing and training parameterization is developed, based on the heuristic optimization method of genetic algorithms. Furthermore, the ways specific stress situations of the plants influence the root-zone microenvrironment are explored. Sensor and actuator faults are detected and diagnosed in sufficient time that the fault detection model can be applied on-line as a reliable supervisor of the operation of an unattended deep-trough hydroponic system. Biological faults were not detected in general. The only biological fault that was detected was related to the transpiration of the plants. It seems that the interaction between plants and their root-zone microenvironment is not equally balanced, as the condition of the plants is highly influenced by the conditions in their root zone microenvironment, while these microenvironment conditions (as they are represented by the measurable variables) are not influenced in the same degree by the conditions of the plants. Finally, the genetic algorithm system developed here can be successfully applied to a combinatorial problem such as deciding the best neural network architecture, activation functions and training algorithm for a specific model.

ISBN: 0493502947Subjects--Topical Terms:

1019504
Engineering, Agricultural.

Neural network fault detection and diagnosis in deep-trough hydroponic systems.
LDR:02478nam 2200277 a 45 001 934958
005 20110509
008 110509s2002 eng d
020 $a 0493502947
035 $a (UnM)AAI3037245
035 $a AAI3037245
040 $a UnM $c UnM
100 1 $a Ferentinos, Konstantinos P. $3 1258652
245 1 0 $a Neural network fault detection and diagnosis in deep-trough hydroponic systems.
300 $a 209 p.
500 $a Adviser: Louis D. Albright.
500 $a Source: Dissertation Abstracts International, Volume: 62-12, Section: B, page: 5822.
502 $a Thesis (Ph.D.)--Cornell University, 2002.
520 $a A system for real-time detection and diagnosis of specific mechanical, sensor and plant (biological) failures is developed. The feedforward neural network methodology is used as the main tool for the development of the fault detection model and its capabilities are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network designing and training parameterization is developed, based on the heuristic optimization method of genetic algorithms. Furthermore, the ways specific stress situations of the plants influence the root-zone microenvrironment are explored. Sensor and actuator faults are detected and diagnosed in sufficient time that the fault detection model can be applied on-line as a reliable supervisor of the operation of an unattended deep-trough hydroponic system. Biological faults were not detected in general. The only biological fault that was detected was related to the transpiration of the plants. It seems that the interaction between plants and their root-zone microenvironment is not equally balanced, as the condition of the plants is highly influenced by the conditions in their root zone microenvironment, while these microenvironment conditions (as they are represented by the measurable variables) are not influenced in the same degree by the conditions of the plants. Finally, the genetic algorithm system developed here can be successfully applied to a combinatorial problem such as deciding the best neural network architecture, activation functions and training algorithm for a specific model.
590 $a School code: 0058.
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Engineering, Environmental. $3 783782
690 $a 0539
690 $a 0775
710 2 0 $a Cornell University. $3 530586
773 0 $t Dissertation Abstracts International $g 62-12B.
790 $a 0058
790 1 0 $a Albright, Louis D., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3037245