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商品描述
Guidance for the Verification and Validation of Neural Networks is a supplement to the IEEE Standard for Software Verification and Validation, IEEE Std 1012-1998. Born out of a need by the National Aeronautics and Space Administration's safety- and mission-critical research, this book compiles over five years of applied research and development efforts. It is intended to assist the performance of verification and validation (V&V) activities on adaptive software systems, with emphasis given to neural network systems. The book discusses some of the difficulties with trying to assure adaptive systems in general, presents techniques and advice for the V&V practitioner confronted with such a task, and based on a neural network case study, identifies specific tasking and recommendations for the V&V of neural network systems.
"As the demand for developing and assuring adaptive systems grows, this guidebook will provide practitioners with the insight and practical steps for verifying and validating neural networks. The work of the authors is a great step forward, offering a level of practical experience and advice for the software developers, assurance personnel, and those performing verification and validation of adaptive systems. This guide makes possible the daunting task of assuring this new technology. NASA is proud to sponsor such a realistic approach to what many might think a very futuristic subject. But adaptive systems with neural networks are here today and as the NASA Manager for Software Assurance and Safety, I believe this work by the authors will be a great resource for the systems we are building today and into tomorrow."
-Martha S. Wetherholt, NASA Manager of Software Assurance and Software Safety NASA Headquarters, Office of Safety & Mission Assurance
Preface.Acknowledgements.
1 Overview.
1.1 Definitions and Conventions.
1.2 Organization of the Book.
2 Areas of Consideration for Adaptive Systems.
2.1 Safety-Critical Adaptive System Example and Experience.
2.2 Hazard Analysis.
2.3 Requirements for Adaptive Systems.
2.4 Rule Extraction.
2.5 Modified Life Cycle for Developing Neural Networks.
2.6 Operational Monitors.
2.7 Testing Considerations.
2.8 Training Set Analysis.
2.9 Stability Analysis
2.10 Configuration Management of Neural Network Training and Design.
2.11 Simulation of Adaptive Systems.
2.12 Neural Network Visualization.
2.13 Adaptive System and Neural Network Selection.
3 Verification and Validation of Neural Networks—Guidance.
3.1 Process: Management.
3.2 Process: Acquisition.
3.3 Process: Supply.
3.4 Process: Development.
3.5 Process: Operation.
3.6 Process: Maintenance.
4 Recent Changes to IEEE Std 1012.
Appendix A: References.
Appendix B: Acronyms.
Appendix C: Definitions.
商品描述(中文翻譯)
描述：
《神經網絡的驗證與驗證指導》是IEEE軟體驗證與驗證標準IEEE Std 1012-1998的補充。這本書源於美國國家航空暨太空總署（NASA）在安全和任務關鍵研究中的需求，匯集了超過五年的應用研究和開發努力。其目的是協助自適應軟體系統的驗證與驗證（V&V）活動，特別強調神經網絡系統。本書討論了確保自適應系統的一些困難，為面對此任務的V&V實務工作者提供技術和建議，並基於一個神經網絡案例研究，確定了神經網絡系統的具體任務和建議。
「隨著對開發和確保自適應系統的需求增長，這本指導手冊將為實務工作者提供驗證和驗證神經網絡的見解和實用步驟。作者的工作是一個巨大的進步，為軟體開發人員、保證人員以及執行自適應系統驗證和驗證的人員提供了實用經驗和建議。這本指南使得確保這項新技術的艱巨任務成為可能。NASA自豪地贊助這種現實的方式來處理許多人可能認為非常未來主義的主題。但帶有神經網絡的自適應系統今天已經存在，作為NASA軟體保證與安全經理，我相信作者的這項工作將成為我們今天和未來所建系統的重要資源。」
- Martha S. Wetherholt, NASA 軟體保證與安全經理，NASA 總部，安全與任務保證辦公室
目錄：
前言。
致謝。
1 概述。
1.1 定義與約定。
1.2 本書的組織。
2 自適應系統的考量領域。
2.1 安全關鍵自適應系統範例與經驗。
2.2 危害分析。
2.3 自適應系統的需求。
2.4 規則提取。
2.5 開發神經網絡的修改生命週期。
2.6 操作監控。
2.7 測試考量。
2.8 訓練集分析。
2.9 穩定性分析。
2.10 神經網絡訓練與設計的配置管理。
2.11 自適應系統的模擬。
2.12 神經網絡可視化。
2.13 自適應系統與神經網絡的選擇。
3 神經網絡的驗證與驗證—指導。
3.1 流程：管理。
3.2 流程：獲取。
3.3 流程：供應。
3.4 流程：開發。
3.5 流程：操作。
3.6 流程：維護。
4 IEEE Std 1012 的近期變更。
附錄 A：參考文獻。
附錄 B：縮寫詞。
附錄 C：定義。
