A design method of immune controller based on Varela artificial immune network model

This paper brings a detailed mathematical description of an artificial immune network model, named aiNet. The model is implemented in association with graph concepts and hierarchical clustering techniques, and is proposed to perform machine learning, data compression and cluster analysis. Pictorial representations for the aiNet basic units and typical architectures are introduced. The proposed immune network was primarily compared on a theoretical basis with well-known artificial neural networks. Then, the aiNet was applied to a non-linearly separable benchmark and a real-world problem, and the results were compared with that of the self-organizing feature map and with others already presented in the literature.

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Construction of an optimal immune network model based on the modified swarm algorithm

Journal of Mathematics Mechanics and Computer Science ◽

10.26577/jmmcs-2018-2-402 ◽

2018 ◽

Vol 98 (2) ◽

pp. 77-87

Author(s):

G. A. Samigulina ◽

◽

Zh. A. Massimkanova ◽

Keyword(s):

Network Model ◽

Immune Network ◽

Model Based ◽

Immune Network Model ◽

Swarm Algorithm

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A New Artificial Immune Network Algorithm for Classifying Complex Data

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2009.00309 ◽

2010 ◽

Vol 32 (3) ◽

pp. 515-521 ◽

Cited By ~ 4

Author(s):

Ruo-chen Liu ◽

Man-chun Niu ◽

Li-cheng Jiao

Keyword(s):

Complex Data ◽

Immune Network ◽

Artificial Immune ◽

Network Algorithm ◽

Artificial Immune Network

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Identification of cavitation in centrifugal pump by artificial immune network

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/09544089211028402 ◽

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.

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