Method for improving ant colony algorithm optimization support vector machine parameters

Abstract

The invention relates to a method for improving ant colony algorithm optimization support vector machine parameters. The method includes the steps that the value range of n parameters is determined, and each parameter is equally divided into N parts to calculate the grid interval; ants select N grid points from the first row to the Nth row, the travel of the N grid points serves as one solution, and M ants find out M solutions; the M solutions are input into an objective function, and a largest objective function value and a smallest objective function value are found out; global information element updating is performed, Pt=Pt-1*rho, a certain number of information element values are added within a certain range near to the globally optimal solution according to the formula Pt=Pt-1-op, and the globally optimal solution is strengthened; a certain number of information element values are reduced within a certain range near to the globally worst solution according to the formula Pt=Pt-1-wp, and the globally worst solution is weakened; if the globally largest cycle index is not reached, the grids are redistricted again until the loop termination conditions are met and optimization of the parameters is completed. The method improves the speed and the accuracy rate for searching for the optimal combination. The principle of grids and high-probability random selection is fused in the method, and the sensitiveness of the ants on the optimal solution is increased.

Description

technical field [0001] The invention relates to a method for optimizing parameters of a support vector machine by an improved ant colony algorithm used for fault diagnosis of mechanical bearings. Background technique [0002] In modern production, more and more attention is paid to the fault diagnosis technology of mechanical equipment. If a certain equipment fails to find and eliminate it in time, the result will not only cause damage to the equipment itself, but may even cause serious consequences of machine crash and death. . The loss caused by the failure of a certain equipment and the failure of the products produced by the entire production line or even the suspension of production is huge. Therefore, the status of fault diagnosis in the production line cannot be ignored. [0003] Rolling bearings are widely used in mechanical equipment and serve as key components. Rolling bearings need to have high reliability, and the occurrence of bearing faults during mechanical...

AI Technical Summary

Problems solved by technology

For example, expert system artificial neural network, etc. These methods are based on the principle of empirical risk minimization, and have some common shortcomings, such as easy to fall into local optimal solution, slow convergence speed, over-learning, etc., especially when the number of samples is limited. Generalization

In most cases, in the fault diagnosis solved by artificial intelligence, the lack of fault samples is the bottleneck problem of diagnosis

Too low generalization ability may lead to wrong fault diagnosis results

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