Journal ArticleDOI
Emergent damage pattern recognition using immune network theory
Bo Chen,Chuanzhi Zang +1 more
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TLDR
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.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 immune-network-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.read more
Citations
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Journal ArticleDOI
A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring
TL;DR: A bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases is introduced.
Data Driven Methodology Based on Artificial Immune Systems for Damage Detection
TL;DR: A methodology for structural damage detection using a type of artificial intelligence that is called artificial immune system is presented and results show that the proposed methodology allows to detect damages in the experimental setup.
Journal ArticleDOI
An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms
TL;DR: A framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.
References
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TL;DR: The theory of natural immune system is first briefly introduced and several representative artificial immune networks are discussed, and their principles and learning algorithms are given here in details.
Journal ArticleDOI
Determining the number of clusters in cluster analysis
My Young Cheong,Hakbae Lee +1 more
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Book ChapterDOI
Artificial immune systems: an emergent technology for autonomous intelligent systems and data mining
TL;DR: This work aims to prove why AIS are of interest, starting from the real-world of applications that is asking for a radical change of the information systems framework, and describes the main behavioral features of AIS — as self-maintenance, distributed and adaptive computational systems.
Proceedings ArticleDOI
Artificial Immune Networks: Models and Applications
TL;DR: The theory of natural immune system is first briefly introduced and several representative artificial immune networks are discussed, and their principles and learning algorithms are given here in details.
Proceedings ArticleDOI
Clonal Selection-Based Neural Classifier
TL;DR: This work is a first attempt of applying the clonal selection principle to the training of multi-layer perceptrons (MLPs) and the proposed classifier is tested against a set of benchmark problems and yields promising results.