Automatic Image Classification Using the Classification Ant-Colony Algorithm
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03
A Gabor atom network for signal classification with application inradar target recognition
IEEE Transactions on Signal Processing
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To enhance plate quality of cold rolling strip steel, a method based on Ant Colony Optimization with Quantum Action (ACO-QA) is developed. In this method, each ant position is represented by a group of quantum bits, and a new quantum rotation gates are designed to update the position of the ant. In order to makes full efficiency, a pretreatment using fuzzy method is firstly adapted before resolving the mathematical model with ACO-QA. This method overcomes the shortcoming of ACO, which is easy to fall into local optimums and has a slow convergence rate in continuous space. At last, a field cognition system is designed to test the efficiency of this method. The results show that it can validly identify almost all defection patterns, compared to traditional identification system. The recognition precision of this method is higher and can meet the shape recognition requirements of cold rolling strip steel.