An immune network model and its applications to process diagnosis

✍ Scribed by Yoshiteru Ishida

• Publisher: John Wiley and Sons
• Year: 1993
• Tongue: English
• Weight: 573 KB
• Volume: 24
• Category: Article
• ISSN: 0882-1666
• DOI: 10.1002/scj.4690240604

✦ Synopsis

Abstract

Immune systems, like neural systems, have a highly sophisticated capability of pattern recognition. However, their recognition and learning mechanisms are quite different from those of a neural system. Neural network models (connectionist models) are information models, which derive partially from the study of the mechanism.

In the same manner, an information model is proposed which derives partially from the recognition mechanism of immune systems, and the learning algorithms on the model are studied.

This paper also proposes some extensions of the model so that it can be applied to process diagnostic problems. The instrumentation system with this immune network (sensor network) can eliminate abnormal information from faulty sensors autonomously. Other than diagnostic problems, the immune network model is potentially applicable to mutual/group test of a set of VLSI of the same type, computer network immune to the virus.