A q-Gaussian SOM neural network and its application for evaluation of the effectiveness of radar ECCM

In order to increase the output space of neighborhood functions and enhance the neighborhood cooperation between neurons,a q-Gaussian self-organizing mapping(SOM) neural network was proposed for evaluation of the effectiveness of radar electronic counter-countermeasures(ECCM).A q-Gaussian function was taken as a neighborhood function in an SOM neural network,and the non-extensive entropic index q was larger to efficiently increase the output space of the q-Gaussian function.The non-extensive entropic index q was adjusted adaptively from large to small with the decreasing neighbor to balance the neurons' distant and close neighborhood cooperation ability.The simulation results of the effectiveness evaluation of the radar ECCM and instance tests show that the q-Gaussian SOM neural network can obtain 100% accurate results in evaluating effectiveness,a 5% higher accuracy rate both in clustering and classification than other SOM neural networks in pattern recognition;the validity and feasibility of the method are verified.