Abstract
To overcome the disadvantage of poor antiinterference ability and uncertain approaching ranks in traditional flatness pattern recognition, then earn essprinciple of Euclidean distance is applied to classify the flatness pattern and to complete the pattern recognition of flatness signal according to the fuzzy classification theory. On this basis, in order to improve the recognition accuracy, quantum ant colony theory is applied to the pattern recognition of flatness and to optimize the result of pattern recognition. Compared with the simplex optimization method, the validity of quantum ant colony theory applied in flatness pattern recognition is testified. This method can improve the recognition accuracy, the result after optimization can accurately control the flatness adjusting sets to meet the need of high precision flatness control.
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