基于模糊对向传播网络的水中目标分类识别

把对向传播（CP）网络的竞争层神经元输出函数定义为模糊隶属度函数,将模糊C-均值（FCM）算法和对向传播网络相结合,提出了一种改进的模糊对向传播（MFCP）网络。在MFCP网络中解决了模糊隶属度函数的自动生成问题。对海上实录的三类水中目标辐射噪声进行了调制解调谱（DEMON谱）的线谱和连续谱分析,并进行了对应的特征提取和神经网络分类识别实验,结果证明：MFCP网络的分类能力及对未训练目标的适应性优于CP网络和误差反向传播（BP）网络。

Underwater target classification based on fuzzy counter propagation network

A Modified Fuzzy Counter Propagation(MFCP) neural network,which is a generalized model of the Counter Propagation(CP) network,is proposed in this paper by defining output of the competitive unit of CP network as a fuzzy membership function and combining Fuzzy C-Mean(FCM) algorithm.The problem,automatic generation of fuzzy membership function has been solved in MFCP network.The classification recognition experiment for three different classes of target noises in the sea was extracted feature from line spectrum and continuous spectrum of DEMON,then performed with neural network,and the results show that MFCP network has higher correct recognition rate and better adaptability to untrained targets than CP network or BP network.