Approach Based on Cloud Model and Vector Neural Network for Emitter Identification
Yiyu Zhou · Dianzi xuebao · 2010
To deal with the problem of emitter identification caused by the vector neural network(VNN),which is incapable of processing the linguistic information and considering the reliability of the training samples in training phases,this paper proposes a new identification method based on cloud model and vector neural network(CMVNN).The new method,which utilizes the cloud model to realize the transformation from qualitative concepts to their quantitative interval expressions,can make use of the improved vector neural network to come true the nonlinear mapping between the interval-value input data and the interval-value output emitter types.A number of simulations are presented to demonstrate the performance of the CMVNN algorithm,including processing 3-type emitter identification problem. Simulation results show that the CMVNN algorithm not only processes the linguistic and numerical input data,but also has higher identification rate in environment with measure errors.