A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

To adapt to the new requirement of the developing flatness control theory and technology, cubic patten were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratii cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to ove come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regre sion (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVI was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhai cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorith with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computatior complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate tl LOO error of LS-SVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling m practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define tl magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.