Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Clonal Selection-Based Neural Classifier
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Agent-based artificial immune system approach for adaptive damage detection in monitoring networks
Journal of Network and Computer Applications
Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks
Journal of Network and Computer Applications
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Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR) approach for the damage classification in structures. An AIPR-based structure damage classifier has been developed, which incorporates several novel characteristics of the natural immune system. The structure damage pattern recognition is achieved through mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning. The damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to evolve its pattern recognition antibodies towards the goal of matching the training data. In addition, the immune learning algorithm can learn and remember different data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group and a three-story frame provided by Los Alamos National Laboratory. The validation results show that the AIPR-based pattern recognition is suitable for structure damage classification. The presented research establishes a fundamental basis for the application of the AIPR concepts in the structure damage classification.