Probabilistic Learning in Immune Network: Weighted Tree Matching Model

1996 ◽  
Vol 3 (1) ◽  
pp. 143-162 ◽  
Author(s):  
RAJANI R. JOSHI ◽  
K. KRISHNANAND
Keyword(s):  
Immune Network ◽  
Probabilistic Learning ◽  
Weighted Tree ◽  
Matching Model

New topics on immunology. 2. Immunoglobulin and immune network.

1985 ◽  
Vol 47 (2) ◽  
pp. 286-291
Author(s):  
SHOICHI ASAHI
Keyword(s):  
Immune Network

Rhythm Based Motion-Music Matching Model

2010 ◽  
Vol 22 (6) ◽  
pp. 990-996 ◽  
Author(s):  
Rukun Fan ◽  
Jing Fu ◽  
Silei Cheng ◽  
Xiang Zhang ◽  
Weidong Geng
Keyword(s):  
Matching Model

Sectoral Differences in Managerss Compensation: Insights from a Matching Model

2015 ◽  
Author(s):  
Emanuela Ciapanna ◽  
Marco Taboga ◽  
Eliana Viviano
Keyword(s):  
Matching Model ◽  
Sectoral Differences

Identification of cavitation in centrifugal pump by artificial immune network

2021 ◽  
pp. 095440892110284
Author(s):  
Seyed M Matloobi ◽  
Mohammad Riahi
Keyword(s):  
Immune System ◽  
Production Systems ◽  
Principal Component ◽  
Immune Network ◽  
Centrifugal Pumps ◽  
Artificial Immune ◽  
Human Immune System ◽  
Time Frequency ◽  
Artificial Immune Network ◽  
Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

scholarly journals Deep Learning Visualization for Underspecification Analysis in Product Design Matching Model Development

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Noptanit Chotisarn ◽  
Wissarut Pimanmassuriya ◽  
Sarun Gulyanon
Keyword(s):  
Deep Learning ◽  
Product Design ◽  
Model Development ◽  
Matching Model
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