A study on intelligent diagnostic method of short-wave receiving system based on SAFOA-LSSVM

The short-wave receiving system has been short of intellectualized automatic monitoring technology and means for a long time, which makes it difficult to realize real-time evaluation and warning of the receiving effect of the equipment. To alleviate the burden of the basic communication security personnel and to provide reliable data and technical support for the analysis and evaluation of the health status of the short-wave receiving system and for the maintenance support of the equipment, The paper adopts SAFOA (Self - adaptive fruit fly optimization algorithm, SAFOA) and combine with K-fold Cross Validation (K-CV) to optimize key parameters of LSSVM (Least squares support vector machine, LSSVM) and to establish classifier of the Self-adaptive fruit fly least squares support vector machine and finally to implement the intelligent diagnosis of the short-wave receiving system. Experimental simulation shows that SAFOA has avoided the time-consuming nature and blindness of Grid Search and FOAu0027s disadvantage of being easy to fall into local optimum. As can be seen, the SAFOA-LSSVM model is obviously better than GS-LSSVM model and FOA-LSSVM model.