基于动量自适应BP神经网络的鸢乌贼模式识别

doi:10.11978/2020135

热带海洋学报 ›› 2021, Vol. 40 ›› Issue (6): 102-110.doi: 10.11978/2020135CSTR: 32234.14.2020135

基于动量自适应BP神经网络的鸢乌贼模式识别

杨柳青青¹(), 储莫闲², 刘必林^2,^3,^4,^5,⁶(), 孔祥洪^2,³

1.上海海洋大学信息学院, 上海 201306
2.上海海洋大学海洋科学学院, 上海 201306
3.国家远洋渔业工程技术研究中心, 上海 201306
4.农业农村部大洋渔业开发重点实验室, 上海201306
5.农业农村部大洋渔业资源环境科学观测实验站, 上海201306
6.大洋渔业资源可持续开发教育部重点实验室, 上海 201306

收稿日期:2020-11-17 修回日期:2021-02-27 出版日期:2021-11-10 发布日期:2021-03-04
通讯作者: 刘必林
作者简介:杨柳青青(1999—), 女, 上海市人。email: 254147697@qq.com
基金资助:
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)

Pattern recognition of Sthenoteuthis oualaniensis based on BPNN about momentum and self-adaption

YANG Liuqinqing¹(), CHU Moxian², LIU Bilin^2,^3,^4,^5,⁶(), KONG Xianghong^2,³

1. College of Information, Shanghai Ocean University, Shanghai 201306, China
2. College of marine science, Shanghai Ocean University, Shanghai 201306, China
3. National Distant-water Fisheries Engineering Research Center, Shanghai 201306, China
4. Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
5. Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
6. The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China

Received:2020-11-17 Revised:2021-02-27 Online:2021-11-10 Published:2021-03-04
Contact: LIU Bilin
Supported by:
National Key R&D Program of China(2019YFD0901404);National Nature Science Foundation of China(41876141);The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning under contract(0810000243);Capacity building project of local universities of Shanghai Municipal Science and Technology Committee(20050501800);Shanghai Science and technology innovation action plan(19DZ1207502)

摘要/Abstract

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

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.

Key words: back propagation neural network, Sthenoteuthis oualaniensis, beak, population discrimination, pattern recognition

中图分类号:

TP183

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.

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.

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形态参数	极大值				极小值
形态参数	原始极大值	标准化极大绝对值	样本编号	海域	原始极小值	标准化极小绝对值	样本编号	海域
胴长(ML)	525	1	42	3	80	0.011236	144	1
上头盖长(UHL)	39.56	1	42	3	5.44	0.001758	464	1
上脊突长(UCL)	50.77	1	162	1	4.44	0.002806	146	1
上喙长(URL)	12.92	1	30	3	1.77	0.000897	464	1
上侧壁长(ULWL)	45.33	1	42	3	5.68	0.01488	154	1
上翼长(UWL)	12.64	1	186	3	1.67	0.004558	144	1
下头盖长(LHL)	11.63	1	200	3	1.66	0.003009	298	1
下脊突长(LCL)	24.1	1	26	3	2.64	0.002796	184	1
下喙长(LRL)	12.86	1	303	1	1.47	0.00439	464	1
下侧壁长(LLWL)	35.67	1	42	3	4.62	0.00161	23	1
下翼长(LWL)	22.1	1	42	3	2.25	0.012594	327	1

I层	H层
I层	H₁	H₂	H₃	H₄	H₅	H₆	H₇	H₈	H₉
ML	0.51787	1.76012	1.17685	0.96851	-0.62503	-0.39972	-2.30729	-2.41620	-1.21233
UHL	1.02004	-0.11701	0.78299	0.97829	0.58829	1.06385	-1.69839	-0.67041	1.38657
UCL	0.20084	2.43943	-0.47049	-1.96293	-0.06245	-1.57966	-0.29600	0.53659	0.31492
URL	-0.04119	0.14562	-0.99725	-1.18086	-1.72815	-0.48685	1.37671	-1.35249	-1.31960
ULWL	1.04319	-1.37696	-2.54213	2.98592	-1.81316	1.65405	-1.53345	-0.57482	0.30500
UWL	-0.02106	1.29860	0.10095	-1.31129	-1.31384	0.01470	-0.91160	0.56923	1.04699
LHL	-1.62204	0.94419	-1.77699	1.38963	0.65958	-1.69024	0.19457	1.55272	-0.54919
LCL	0.74875	0.65564	0.55693	0.92801	-1.63957	-1.29223	0.28282	-0.22000	1.34480
LRL	0.61926	-0.12646	-1.45393	0.27165	0.40933	-0.14337	0.97641	-1.35238	1.06185
LLWL	-1.90206	1.97220	0.89296	0.47994	-1.69910	-1.48343	0.13988	0.69561	-1.31014
LWL	0.60720	-0.28580	-1.70620	1.63012	-1.03251	-0.77968	1.41782	0.98721	-0.59789

H层	O层
H层	西北印度洋	中东太平洋	南海
H1	0.16992	-0.24264	-0.10120
H2	-0.55120	2.30283	-1.69967
H3	-0.30025	2.32027	-2.03169
H4	1.84776	-1.90292	0.03078
H5	-0.15796	0.22273	-0.12153
H6	1.05078	-1.17558	0.15734
H7	-0.47428	-1.20609	1.73593
H8	-0.28751	-1.01167	1.41477
H9	0.44897	0.21886	0.99420

基于动量自适应BP神经网络的鸢乌贼模式识别

Pattern recognition of Sthenoteuthis oualaniensis based on BPNN about momentum and self-adaption

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参考文献 24

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海区	样本总数	识别样本数			识别率
海区	样本总数	西北印度洋	中东太平洋	南海	识别率
西北印度洋	90	80	8	2	88.89%
中东太平洋	189	2	170	17	89.95%
南海	219	0	7	212	96.80%

学习方法	各海区识别率			总识别率
学习方法	西北印度洋	中东太平洋	南海	总识别率
梯度下降法	46.15%	70.37%	88.24%	74.32%
单一动量法	53.85%	70.37%	91.12%	77.03%
单一自适应法	84.62%	81.48%	94.12%	87.84%
动量自适应法	100%	88.89%	94.12%	93.24%

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