Emergent damage pattern recognition using immune network theory

Bo Chen , Chuanzhi Zang

Abstract

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immunenetwork-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

Key Words

emergent pattern recognition; immune network theory; hierarchical clustering; artificial immune systems.

Address

Bo Chen — Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Drive, Houghton, MI 49931, USA, Department of Electrical and Computer Engineering, Michigan Technological University, USA
Chuanzhi Zang — Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Drive, Houghton, MI 49931, USA, Shenyang Institute of Automation, Chinese Academy of Science, Nanta Street 114, Shenyang, Liaoning, P.R. China, 110016

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