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武器裝備備件需求預(yù)測

武器裝備備件需求預(yù)測

定 價:¥98.00

作 者: 趙建忠 等
出版社: 電子工業(yè)出版社
叢編項:
標(biāo) 簽: 暫缺

ISBN: 9787121317538 出版時間: 2018-10-01 包裝: 平裝
開本: 16開 頁數(shù): 304 字?jǐn)?shù):  

內(nèi)容簡介

  本書著重介紹了武器裝備備件的基本概念、需求影響因素、需求預(yù)測的程序及武器裝備備件保障決策時常用的需求預(yù)測方法,主要包括基于相似分析法、模糊綜合評判法、灰色評估法的備件品種確定方法,基于仿真、時間序列、灰色模型、支持向量機(jī)、故障分析、組合模型的備件需求預(yù)測方法。

作者簡介

  趙建忠,男,1978年出生,山東聊城人,中校軍銜,技術(shù)9級,現(xiàn)任海軍航空大學(xué)教員。2002年畢業(yè)于山東理工大學(xué),本科畢業(yè)后分配至海軍航空兵部隊工作,2006年考入海軍航空工程學(xué)院攻讀碩士、博士研究生,2013年調(diào)入海軍航空工程學(xué)院工作。近5年,主持科研項目5項,獲軍隊科技進(jìn)步二、三獎各1項,以**作者發(fā)表學(xué)術(shù)研究論文50余篇,SCI檢索1篇,EI檢索12篇,連續(xù)4年被學(xué)校評為"學(xué)術(shù)先進(jìn)個人”。

圖書目錄

第1 章 概述 ·················································································· 1

1.1 基本概念 ··········································································· 1

1.2 備件分類 ··········································································· 3

1.2.1 常見的備件分類方法 ···················································· 3

1.2.2 低消耗備件 ································································ 4

1.2.3 基于模糊隸屬度的低消耗備件定義與界定 ························· 4

1.3 備件需求影響因素分析 ······················································· 10

1.3.1 備件需求內(nèi)在影響因素分析 ········································· 11

1.3.2 備件需求外在影響因素分析 ········································· 12

1.3.3 影響因素的量化及規(guī)范化處理 ······································ 16

1.3.4 基于粗糙集屬性約簡的主要影響因素確定 ······················· 20

1.4 備件需求層次性分析 ·························································· 22

1.4.1 備件層次需求分析 ····················································· 22

1.4.2 裝備系統(tǒng)層次需求分析 ··············································· 23

1.4.3 維修保障組織需求分析 ··············································· 23

1.5 備件需求特點分析 ····························································· 23

1.5.1 備件需求特征 ··························································· 23

1.5.2 常消耗備件需求特點 ·················································· 24

1.5.3 低消耗備件需求特點 ·················································· 25

第2 章 武器裝備備件需求預(yù)測與配置優(yōu)化基礎(chǔ) ··································· 26

2.1 備件消耗模式分析 ····························································· 26

2.2 備件需求預(yù)測的基本原則 ···················································· 27

2.3 備件需求預(yù)測的一般程序 ···················································· 29

2.4 備件需求預(yù)測的方法 ·························································· 31

2.4.1 定性備件需求預(yù)測方法 ··············································· 31

2.4.2 定量備件需求預(yù)測方法 ··············································· 32

2.4.3 模型復(fù)雜性與模型選擇 ··············································· 34

第3 章 武器裝備備件品種確定方法 ·················································· 36

3.1 概述 ··············································································· 36

3.2 基于相似分析法的武器裝備備件品種確定方法 ·························· 42

3.2.1 相似分析法簡介 ························································ 42

3.2.2 實例分析 ································································· 44

3.3 基于模糊層次綜合評判的備件品種確定方法 ····························· 46

3.3.1 模糊層次綜合評判法簡介 ············································ 46

3.3.2 模型建立 ································································· 47

3.3.3 示例分析 ································································· 50

3.4 基于D-S 理論和模糊綜合評判的備件品種確定方法 ··················· 52

3.4.1 模型建立 ································································· 52

3.4.2 實現(xiàn)步驟 ································································· 54

3.4.3 示例分析 ································································· 57

3.5 基于灰色評估法的武器裝備備件品種確定方法 ·························· 61

3.5.1 評價指標(biāo)體系的建立 ·················································· 61

3.5.2 評價模型的建立 ························································ 63

3.5.3 示例分析 ································································· 65

第4 章 基于壽命分布和仿真的武器裝備備件需求預(yù)測方法 ···················· 69

4.1 基于壽命分布的武器裝備備件需求預(yù)測方法 ····························· 69

4.1.1 模型的假設(shè) ······························································ 70

4.1.2 模型的建立 ······························································ 70

4.1.3 示例分析 ································································· 74

4.2 基于蒙特卡羅仿真的備件需求預(yù)測方法 ·································· 77

4.2.1 模型的假設(shè) ······························································ 78

4.2.2 參數(shù)確定 ································································· 78

4.2.3 蒙特卡羅方法 ··························································· 79

4.2.4 仿真模型的建立 ························································ 79

4.2.5 實例分析 ································································· 81

4.3 基于馬爾可夫與蒙特卡羅仿真的備件需求預(yù)測方法 ···················· 82

4.3.1 馬爾可夫預(yù)測模型 ····················································· 82

4.3.2 備件消耗的馬爾可夫性分析 ········································· 84

4.3.3 基于馬爾可夫與蒙特卡羅仿真預(yù)測模型的建立 ················· 85

4.3.4 實例分析 ································································· 88

4.4 基于Logistic 回歸和馬爾可夫過程的備件需求 預(yù)測仿真模型 ······· 89

4.4.1 模型建立 ································································· 90

4.4.2 需求發(fā)生概率預(yù)測 ····················································· 91

4.4.3 需求數(shù)量預(yù)測 ··························································· 93

4.4.4 誤差分析 ································································· 96

4.4.5 示例分析 ································································· 97

4.5 考慮設(shè)備停機(jī)的備件需求預(yù)測仿真模型 ································· 100

4.5.1 基于最大熵原理與概率加權(quán)矩的備件壽命分布確定 ·········· 101

4.5.2 基于保障度的間斷工作備件需求預(yù)測模型建立 ················ 106

4.5.3 仿真思路及流程規(guī)劃 ················································· 109

4.5.4 示例分析 ································································ 111

第5 章 基于時間序列和回歸分析的武器裝備備件需求預(yù)測方法 ············· 115

5.1 時間序列法概述 ······························································· 115

5.2 基于時間序列法的備件需求預(yù)測 ·········································· 116

5.2.1 算數(shù)平均預(yù)測法 ······················································· 116

5.2.2 移動平均預(yù)測法 ······················································· 117

5.2.3 指數(shù)平滑預(yù)測法 ······················································· 118

5.2.4 示例分析 ································································ 122

5.3 基于回歸分析的備件需求預(yù)測方法 ······································· 126

5.3.1 線性回歸概述 ·························································· 126

5.3.2 一元線性回歸模型 ···················································· 127

5.3.3 多元線性回歸模型 ···················································· 129

5.3.4 示例分析 ································································ 131

第6 章 基于灰色模型的武器裝備備件需求預(yù)測方法 ···························· 133

6.1 灰色預(yù)測方法概述 ···························································· 133

6.1.1 灰色系統(tǒng)簡介 ·························································· 133

6.1.2 灰色系統(tǒng)理論基礎(chǔ) ···················································· 134

6.1.3 灰色預(yù)測方法 ·························································· 136

6.2 基于一般序列GM(1,1)模型的備件需求預(yù)測方法 ······················ 137

6.2.1 模型建立 ································································ 137

6.2.2 示例分析 ································································ 141

6.3 基于含有空缺值序列GM(1,1)模型的備件需求預(yù)測方法 ········· 144

6.3.1 模型建立 ································································ 144

6.3.2 示例分析 ································································ 145

6.4 基于加入影響因子改進(jìn)灰色模型的備件需求預(yù)測方法 ················ 146

6.4.1 改進(jìn)灰色預(yù)測模型的建立 ··········································· 146

6.4.2 示例分析 ································································ 147

6.5 基于灰色-馬爾可夫模型的備件需求預(yù)測方法 ··························· 155

6.5.1 經(jīng)典灰色-馬爾可夫預(yù)測模型 ······································· 155

6.5.2 改進(jìn)的灰色-馬爾可夫模型 ·········································· 158

6.5.3 示例分析 ································································ 162

第7 章 基于人工智能的武器裝備備件需求預(yù)測方法 ···························· 166

7.1 基于BP 神經(jīng)網(wǎng)絡(luò)的備件需求預(yù)測方法 ·································· 166

7.1.1 BP 神經(jīng)網(wǎng)絡(luò)基本原理 ··············································· 167

7.1.2 預(yù)測模型的建立 ······················································· 169

7.1.3 示例分析 ································································ 170

7.2 基于支持向量機(jī)(SVM)的備件需求預(yù)測方法 ························ 175

7.2.1 支持向量機(jī)概述 ······················································· 176

7.2.2 支持向量機(jī)預(yù)測原理 ················································· 177

7.2.3 支持向量機(jī)預(yù)測模型 ················································· 185

7.2.4 示例分析 ································································ 188

7.3 基于復(fù)雜時間序列的組合相關(guān)向量機(jī)備件需求預(yù)測方法 ············· 192

7.3.1 相空間重構(gòu) ····························································· 192

7.3.2 小波變換基本理論 ···················································· 193

7.3.3 小波函數(shù)的選取 ······················································· 198

7.3.4 基于組合相關(guān)向量機(jī)的預(yù)測原理 ································ 199

7.3.5 示例分析 ································································ 206

第8 章 基于故障分析的武器裝備備件需求預(yù)測方法 ···························· 213

8.1 故障預(yù)測的基本原理分析 ··················································· 213

8.2 基于故障預(yù)測的備件需求預(yù)測系統(tǒng)構(gòu)建 ································· 215

8.2.1 工作流程 ································································ 216

8.2.2 數(shù)據(jù)預(yù)處理 ····························································· 216

8.2.3 故障預(yù)測 ································································ 219

8.2.4 備件需求確定 ·························································· 220

8.3 基于信息融合和IMUGM(1,1)故障預(yù)測的備件需求預(yù)測方法 ········ 220

8.3.1 問題描述 ································································ 221

8.3.2 IMUGM(1,1)模型的建立 ············································ 221

8.3.3 基于信息融合和IMUGM(1,1)故障預(yù)測的備件需求

預(yù)測模型構(gòu)建 ·························································· 224

8.3.4 示例分析 ································································ 225

8.4 基于信息融合和IMUGM(1,m,w)故障預(yù)測的備件需求預(yù)測建模 ···· 230

8.4.1 問題描述 ································································ 231

8.4.2 IMUGM(1,m,w)模型的建立 ········································· 231

8.4.3 基于信息融合和IMUGM(1,m,w)故障預(yù)測的備件需求

預(yù)測模型構(gòu)建 ·························································· 234

8.4.4 示例分析 ································································ 234

8.5 基于故障規(guī)律的備件需求預(yù)測方法 ······································· 239

8.5.1 備件需求量計算公式 ················································· 239

8.5.2 示例分析 ································································ 242

第9 章 基于組合模型的武器裝備備件需求預(yù)測方法 ···························· 244

9.1 組合預(yù)測方法概述 ···························································· 244

9.1.1 一般組合預(yù)測模型 ···················································· 245

9.1.2 最優(yōu)加權(quán)系數(shù)的確定 ················································· 246

9.1.3 模型的檢驗 ····························································· 247

9.2 基于回歸分析和灰色模型的備件需求預(yù)測方法 ························· 247

9.2.1 單個預(yù)測模型建立 ···················································· 247

9.2.2 最優(yōu)組合預(yù)測模型 ···················································· 248

9.2.3 示例分析 ································································ 248

9.3 基于小波變換的備件需求預(yù)測方法 ······································· 251

9.3.1 小波變換理論 ·························································· 251

9.3.2 GM(1,1)-ARMA 模型的改進(jìn) ········································ 254

9.3.3 WGM -ARMA 模型的構(gòu)建 ·········································· 264

9.3.4 示例分析 ································································ 265

9.4 基于改進(jìn)Theil 不等系數(shù)的備件需求預(yù)測建模 ·························· 270

9.4.1 單項預(yù)測模型的建立 ················································· 271

9.4.2 基于Theil 不等系數(shù)的IOWHA 算子的組合預(yù)測模型構(gòu)建 ··· 275

9.4.3 基于Theil 不等系數(shù)的IOWHA 算子的組合預(yù)測模型改進(jìn) ··· 278

9.4.4 示例分析 ································································ 282

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