Abstract:

In order to overcome the adverse effects on the performance of classification of SVM(Support Vector Machine) model parameters in the random selection, based on chaotic CABC-SVM ( Artificial Bee Colony Algorithm of Support Vector Machine) parameters optimization method is proposed. CABC algorithm uses the Logistic chaotic mapping initialization population and tournament selection strategy, further improves the artificial bee colony algorithm convergence speed and optimization precision, the classification accuracy as the fitness function when using the algorithm of penalty factor and kernel function parameters of SVM was optimized. UCI standard data sets experiments show that CABC algorithm has strong local and global search ability, the optimization of SVM can largely overcome local extremum points to obtain a higher classification accuracy, and can effectively shorten the search time. The method was applied to actual gear fault diagnosis, energy wavelet was used as the feature input SVM, classification accuracy rate reached 99. 4%, verified the feasibility and effectiveness of the improved method in this paper.

Key words: support vector machine ( SVM), chaotic artificial bee colony ( CABC) algorithm, parameter optimization, gear fault diagnosis