View in EDS

Detecting change in dynamic process systems with immunocomputing

Saved in:

Bibliographic Details
Title:	Detecting change in dynamic process systems with immunocomputing
Authors:	Yang, X.¹, Aldrich, C. ca1@sun.ac.za, Maree, C.¹
Source:	Minerals Engineering. Feb2007, Vol. 20 Issue 2, p103-112. 10p.
Subjects:	Immunocomputers, Computer simulation of immune system, Algorithms, Anatomy
Abstract:	Abstract: The natural immune system is an adaptive distributed pattern recognition system with several functional components designed for recognition, memory acquisition, diversity and self-regulation. In artificial immune systems, some of these characteristics are exploited in order to design computational systems capable of detecting novel patterns or the anomalous behaviour of a system in some sense. Despite their obvious promise in the application of fault diagnostic systems in process engineering, their potential remains largely unexplored in this regard. In this paper, the application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self–nonself discrimination problem in immunocomputing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The case studies have indicated that immunocomputing based on negative selection can provide competitive options for fault diagnosis in nonlinear process systems, but further work is required on large systems characterized by many variables. [Copyright &y& Elsevier]
	Copyright of Minerals Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Abstract: The natural immune system is an adaptive distributed pattern recognition system with several functional components designed for recognition, memory acquisition, diversity and self-regulation. In artificial immune systems, some of these characteristics are exploited in order to design computational systems capable of detecting novel patterns or the anomalous behaviour of a system in some sense. Despite their obvious promise in the application of fault diagnostic systems in process engineering, their potential remains largely unexplored in this regard. In this paper, the application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self–nonself discrimination problem in immunocomputing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The case studies have indicated that immunocomputing based on negative selection can provide competitive options for fault diagnosis in nonlinear process systems, but further work is required on large systems characterized by many variables. [Copyright &y& Elsevier]
ISSN:	08926875
DOI:	10.1016/j.mineng.2006.05.012