TY  - CONF
Y1  - 2014///
SN  - 2261236X
PB  - EDP Sciences
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904988619&doi=10.1051%2fmatecconf%2f20141303011&partnerID=40&md5=fb950bebff63532b2cbef68f09a2f279
A1  - Alnaimi, F.B.I.
A1  - A Ali, M.
A1  - Al-Kayiem, H.H.
A1  - Mohamed Sahari, K.S.B.
VL  - 13
CY  - Kuala Lumpur
AV  - none
N2  - This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS) Error has been used to compare the results of one and two hidden layer (1HL), (2HL) ANN structures. © 2014 Owned by the authors, published by EDP Sciences.
N1  - cited By 1; Conference of 4th International Conference on Production, Energy and Reliability, ICPER 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:106620
KW  - Neural networks
KW  -  Execution phasis; Hidden layers; Minimization algorithms; Monitoring techniques; On-line condition monitoring system; Real-time data; Root-mean-square errors; Training algorithms
KW  -  Algorithms
ID  - scholars5077
TI  - On-line condition monitoring system for high level trip water in steam Boiler's Drum
ER  -