| 基于动量自适应BP神经网络的鸢乌贼模式识别 |
| |
| 引用本文： | 杨柳青青,储莫闲,刘必林,孔祥洪.基于动量自适应BP神经网络的鸢乌贼模式识别[J].热带海洋学报,2021,40(6):102-110. |
| |
| 作者姓名： | 杨柳青青 储莫闲 刘必林 孔祥洪 |
| |
| 作者单位： | 1.上海海洋大学信息学院, 上海 2013062.上海海洋大学海洋科学学院, 上海 2013063.国家远洋渔业工程技术研究中心, 上海 2013064.农业农村部大洋渔业开发重点实验室, 上海2013065.农业农村部大洋渔业资源环境科学观测实验站, 上海2013066.大洋渔业资源可持续开发教育部重点实验室, 上海 201306 |
| |
| 基金项目： | 国家重点研发计划(2019YFD0901404);国家自然科学基金面上项目(41876141);上海市高校特聘教授“东方学者”岗位计划项目(0810000243)(0810000243);上海市科委地方高校能力建设项目(20050501800);上海市科技创新行动计划(19DZ1207502) |
| |
| 摘 要： | 近年来, 计算机模式识别技术因其识别结果准确、快速, 而不断被用于生物判别邻域。本文利用MATLAB软件实现动量自适应BP神经网络(back propagation neural networks)对西北印度洋、中东太平洋和南海3个海区的鸢乌贼角质颚及其胴长进行模式识别。研究结果显示, 训练成型的神经网络收敛误差仅为4.416×10-2, 加入动量和自适应学习率的BP神经网络对鸢乌贼地理种群的识别率有显著提高。3个海区的正确识别率分别为100%、88.89%和94.12%, 总成功率为 93.24%, 说明角质颚外部形态和胴长可用于鸢乌贼地理种群的区分。而BP神经网络的其他学习算法, 如梯度下降法、单一动量法和单一自适应法的总识别率分为74.32%, 77.03%和87.84%。本研究的识别效果稳定, 对于大样本训练集的识别率也高达92.77%, 为头足类的种群判别提供了新的方法和思路。
|
| 关 键 词： | BP神经网络 鸢乌贼 角质颚 种群区分 模式识别 |
| 收稿时间： | 2020-11-17 |
| 修稿时间： | 2021-02-27 |
Pattern recognition of Sthenoteuthis oualaniensis based on BPNN about momentum and self-adaption |
| |
| YANG Liuqinqing,CHU Moxian,LIU Bilin,KONG Xianghong.Pattern recognition of Sthenoteuthis oualaniensis based on BPNN about momentum and self-adaption[J].Journal of Tropical Oceanography,2021,40(6):102-110. |
| |
| Authors: | YANG Liuqinqing CHU Moxian LIU Bilin KONG Xianghong |
| |
| Abstract: | In recent years, computer pattern recognition technology has been used in biometric identification for its accurate and rapid recognition capability. In this paper, pattern recognition of beak and mantle length of Sthenoteuthis oualaniensis in the northwestern Indian Ocean, Middle East Pacific and South China Sea was carried out by using momentum adaptive back propagation (BP) neural networks (BPNN) based on MATLAB software. The results showed that the convergence error of the trained neural network was only 4.416×10-2, and the recognition rate of beak of S. oualaniensis was significantly improved by adding momentum and adaptive learning rate to the BPNN. The correct recognition rates were 100% in the northwestern Indian Ocean, 88.89% in the Middle East Pacific and 94.12% in the South China Sea, with a total success rate of 93.24%, which indicates that the external morphology of beak of S. oualaniensis and mantle length can be used to distinguish different geographical populations. The total recognition rates of other BPNN learning algorithms of gradient descent, single momentum and single adaptive method were 74.32%, 77.03% and 87.84%, respectively. The recognition effect of this study was stable, and the recognition rate of large sample training set was as high as 92.77%, which provides a new method for the identification of cephalopod population. |
| |
| Keywords: | back propagation neural network Sthenoteuthis oualaniensis beak population discrimination pattern recognition |
|
| 点击此处可从《热带海洋学报》浏览原始摘要信息 |
| 点击此处可从《热带海洋学报》下载免费的PDF全文 |
|
