基于动量自适应BP神经网络的鸢乌贼模式识别

doi:10.11978/2020135

Abstract

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

CLC Number:

TP183

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.

Figures/Tables 10

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References 24

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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

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%

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

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