【摘要】 本系列文章的工作是在舰船噪声谱图的基础上，利用模糊神经网络对舰船进行分类识别。本文是系列文章的第四篇，研究模糊神经网络用于识别分类．选用了多层前馈神经网络和BP学习算法，推导了学习过程中模糊器参数的调整公式，最后给出1049个样本（41条舰船，63种工况，原始记录长约3．5小时）的识别分类结果，识别正确率大于92％。更多还原

【Abstract】 This series of papers deal with ship-target recognition. The project is conducted by usingfuzzy neural networks and basing on the spectra of ship radiated-noise. Based on the studies of alarge amount of ship radiated-noise data, which has been collected from actual ships on the seat weextracted effectively recognizable features. Such features include line-spectrum features, stationary andnonstationary spectrum features as well as rhythm features. Finally the categorization are tested byunknown samples on the sea, including, 33 surface ships, 8 underwater targets in 30 operating conditions.Methods for memorization and classification are also explored in the project. Paper (Ⅳ) is the last inthe series. It deals with the application of fuzzy neural network to the recognition of targets. The neuralnetwork is a multi-layer forward network and the learning algorithm is BP (Error Back Propagation).In the paper, the adjust formula of parameter of fuzzier is given. The paper provides a recognition resultwhich is drawn from 1049 samples gathered from 41 ships in 63 operating conditions, with an originalrecording time of about 3.5 hours. Over 92 % of recognitions are correct.更多还原