基于粗集-神经网络模型的目标识别方法研究

Target Recognition Method Based on Rough Set and Neural Network Model

为了克服传统目标识别方法的缺点，建立了一种基于粗集理论和神经网络结合的粗集-神经网络模型，利用粗集对输入信息进行约简，剔除冗余信息，简化了生成规则和神经网络模型结构，提高了网络训练速度和运行速度，并通过仿真实验，证明了该混合模型的可行性。

A network model based on rough set theory and neural network is built to surmount the shortcomings of traditional target recognition means. Rough set is used to predigest the input information, eliminate the redundant information, reduce the rules and the structure of the neural network model and improve the speed of training and running. The simulation result proves the feasibility of this mixed model.