面向战斗力指数定量分析的局部逼近方法

郭恩泽; 刘国彬; 邹永杰; 刘正堂; 孙健; 张洪德

doi:10.11884/HPLPB202537.240163

面向战斗力指数定量分析的局部逼近方法

doi: 10.11884/HPLPB202537.240163

1.
中国人民解放军 63893部队，河南洛阳 471003
2.
中国人民解放军陆军工程大学通信士官学校，重庆 400035

基金项目: 重庆市自然科学基金项目(CSTB2022NSCQ-MSX1257)

详细信息

作者简介:
郭恩泽，g1903632257@163.com

通讯作者:
张洪德，hdzhang264@126.com。

中图分类号: TP183
计量
- 文章访问数: 362
- HTML全文浏览量: 131
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-14
- 修回日期: 2024-08-24
- 录用日期: 2024-08-24
- 网络出版日期: 2024-09-21

A novel local approximation approach for quantitative analysis of combat power index

1.
Unit 63893 of the PLA, Luoyang 471003, China
2.
Communications Sergeant School, Army Engineering University, Chongqing 400035, China

摘要

摘要: 战斗力指数的定量化研究是军队实现信息化建设必须解决的难题。针对战斗力指数研究存在定量研究较少、方法精度较低、鲁棒性不强等问题，以及战斗力指数函数本身为复杂规则主导、多变量数学模型、影响因素强耦合等难以拟合的限制，受模糊逻辑理论中对规则的数学分析方法启发，提出了一种基于局部逼近的战斗力指数函数拟合方法，并结合神经网络强大的自学习和自推导能力，构建了相应的基于径向基神经网络（RBF）的定量计算模型。仿真对比实验表明，该方法比利用全局逼近的方法误差率低约2%和6%，且表现出更强的鲁棒性。该计算方法具有较强的实用性，而且具备向其他军事领域迁移的可能性，具备良好的工程应用前景。
- 战斗力指数 /
- 定量分析 /
- 神经网络 /
- 局部逼近 /
- 模糊逻辑
Abstract: The quantitative study of combat effectiveness index is crucial for the informatization construction of the armed forces. To solve the problems of limits of quantitative research, low method accuracy, and weak robustness in the study of combat effectiveness index, and to break through the limitations of dominating complex rules, multivariate mathematical models, and strong coupling of influencing factors in the combat effectiveness index function, inspired by the mathematical analysis methods of rules in fuzzy logic theory, we proposed a local approximation based method for fitting combat effectiveness index function. Combining the powerful self-learning and self-deduction capabilities of neural networks, we constructed a corresponding quantitative calculation model based on radial basis function (RBF). Simulation comparative experiments show that the proposed method has an error rate of about 2% and 6% lower than the current best performing method using global approximation, and exhibits stronger robustness. Our method has strong practicality, can be migrated to other military fields, and has good engineering application prospects.
- combat effectiveness index /
- quantitative analysis /
- neural network /
- local approximation /
- fuzzy logic

HTML全文

图 1 前馈神经网络结构图

Figure 1. Structure diagram of feedforward neural network

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图 2 战斗力指数计算模型

Figure 2. Calculation model for combat effectiveness index

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图 3 不同方法的性能对比

Figure 3. Performance comparison of different methods

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图 4 不同方法对各样本的误差率对比图

Figure 4. Comparison chart of error rates of different methods for each sample

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表 1 某系统指标与对应战斗力指数（训练样本）

Table 1. A certain system indicator and corresponding combat effectiveness index (training sets)

No.	$ X_1 $	$ X_2 $	$ X_3 $	$ X_4 $	$ X_5 $	$ X_6 $	$ X_7 $	index
1	0.008 0	0.3839	0.4643	0.2578	0.4651	1	1	0.1
2	0.3867	0.6727	0.4041	0.987	0.0361	1	0	0.6567
3	0.6247	0.0751	0.3834	0.9626	0.3681	0	1	0.4802
4	0.8994	0.6203	0.3388	0.8733	0.4711	0	1	0.5627
5	0.7807	0.602	0.5827	0.7677	0.3319	0	0	0.4575
6	0.5801	0.6814	0.4982	0.5691	0.6989	0	1	0.4807
7	0.6861	0.9761	0.5345	0.0551	0.3001	1	0	0.5307
8	0.9918	0.5446	0.1884	0.8012	0.2498	0	1	0.5325
9	0.1517	0.3264	0.9244	0.4749	0.7514	1	1	0.6498
10	0.006 0	0.3241	0.1342	0.0568	0.2861	1	1	0.1
11	0.3237	0.6433	0.7615	0.4408	0.4257	0	1	0.3989
12	0.1183	0.7009	0.8832	0.9238	0.7334	1	1	0.6995
13	0.3798	0.3365	0.1264	0.2612	0.3381	1	1	0.5844
14	0.5788	0.9003	0.5844	0.1539	0.1008	1	0	0.5619
15	0.4714	0.0867	0.4697	0.0271	0.5766	1	0	0.4416
16	0.8938	0.0379	0.7388	0.2354	0.4514	1	0	0.6032
17	0.1562	0.2596	0.2821	0.8051	0.6745	0	1	0.3453
18	0.395 0	0.945	0.0852	0.8615	0.8387	1	0	0.6977
19	0.3299	0.7363	0.8801	0.8847	0.7278	0	1	0.5007
20	0.0009	0.5256	0.4316	0.3064	0.7449	0	0	0.1
21	0.2083	0.896	0.2374	0.3735	0.8784	0	0	0.298
22	0.2117	0.7423	0.8269	0.1996	0.0336	1	0	0.5211
23	0.6431	0.4766	0.1992	0.8318	0.1808	0	0	0.3949
24	0.9829	0.961	0.4124	0.102	0.6919	1	0	0.604
25	0.2151	0.2478	0.8876	0.2154	0.8752	0	1	0.3405
26	0.3991	0.565	0.31	0.3312	0.2412	0	0	0.2705
27	0.8604	0.2769	0.6531	0.3415	0.0994	1	1	0.6933
28	0.4469	0.682	0.826	0.6095	0.7994	1	0	0.7053
29	0.7401	0.6015	0.51	0.3157	0.8019	0	1	0.4477
30	0.001 0	0.5982	0.7531	0.6396	0.8263	0	1	0.1
31	0.1449	0.3029	0.3186	0.0775	0.0949	1	0	0.408
32	0.5117	0.3233	0.8123	0.506	0.633	1	1	0.7331
33	0.8103	0.1576	0.8691	0.5492	0.8843	1	1	0.8037
34	0.2604	0.5971	0.2858	0.7397	0.2944	0	0	0.3059
35	0.1761	0.5774	0.0948	0.9358	0.2587	0	0	0.2748
36	0.3009	0.8167	0.4445	0.0062	0.5587	1	1	0.5218
37	0.3245	0.217	0.7227	0.1615	0.8302	0	0	0.2582
38	0.3474	0.1725	0.3881	0.9169	0.7404	0	0	0.3641
39	0.5416	0.6636	0.7911	0.0836	0.184	1	0	0.527
40	0.002 0	0.3116	0.6413	0.354	0.8512	0	1	0.1
41	0.6394	0.7194	0.0269	0.2757	0.6934	0	0	0.3169
42	0.5142	0.1377	0.0843	0.1952	0.0695	0	0	0.1917
43	0.3289	0.4986	0.5695	0.3578	0.9369	1	1	0.6825
44	0.7178	0.3608	0.9264	0.2859	0.5528	1	1	0.716
45	0.6938	0.1595	0.1447	0.0897	0.5322	0	0	0.2009
46	0.5764	0.3444	0.6178	0.1244	0.3252	1	1	0.5985
47	0.8081	0.0461	0.3649	0.4704	0.7324	0	0	0.3727
48	0.4148	0.3836	0.9863	0.5829	0.1511	1	0	0.6372
49	0.3204	0.2348	0.5684	0.3432	0.2111	1	1	0.6068
50	0.0005	0.3179	0.8168	0.5232	0.3022	0	0	0.1

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表 2 某系统指标与对应战斗力指数（测试样本）

Table 2. A certain system indicator and corresponding combat effectiveness index (testing sets)

	$ X_1 $	$ X_2 $	$ X_3 $	$ X_4 $	$ X_5 $	$ X_6 $	$ X_7 $	index
1	0.0005	0.2067	0.1139	0.871	0.7812	0	1	0.1
2	0.188	0.1795	0.2392	0.7679	0.895	0	1	0.3629
3	0.2353	0.6651	0.2227	0.4676	0.1871	1	1	0.6112
4	0.2476	0.6042	0.6518	0.4204	0.8361	1	1	0.68
5	0.1531	0.0344	0.9427	0.9131	0.8684	1	0	0.6126
6	0.3836	0.9837	0.8784	0.3039	0.7341	1	0	0.6526
7	0.1803	0.2427	0.1258	0.4698	0.4155	1	0	0.5014
8	0.3009	0.9656	0.8944	0.4122	0.7063	1	1	0.7264
9	0.1653	0.7884	0.04	0.1822	0.641	0	1	0.282
10	0.002	0.036	0.3512	0.3792	0.7557	0	0	0.1
11	0.8181	0.0879	0.7492	0.0687	0.9076	0	0	0.2601
12	0.1821	0.7894	0.9551	0.8734	0.2748	1	0	0.6354
13	0.8478	0.0628	0.4018	0.0943	0.3653	1	0	0.501
14	0.2354	0.2679	0.7004	0.521	0.774	0	0	0.3096
15	0.2537	0.2642	0.5144	0.522	0.637	0	1	0.3634
16	0.8409	0.2052	0.5102	0.5375	0.0479	0	0	0.3665
17	0.2454	0.3239	0.093	0.4991	0.2922	1	0	0.5214
18	0.2087	0.0236	0.6018	0.5521	0.5491	0	1	0.3359
19	0.7743	0.213	0.0106	0.1927	0.9038	1	0	0.5665
20	0.004	0.2646	0.0695	0.8439	0.4707	0	1	0.1

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