创新、风险、灵活性和学习,视为最优控制和估算

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION Insight Pub Date : 2024-08-22 DOI:10.1002/inst.12500
William D. (Bill) Schindel
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引用次数: 0

摘要

本文总结了一个广为人知的问题--"嘈杂 "环境中的最优控制和估算--如何提供了一个框架,以促进人们对一个众所周知但掌握较少的问题--系统创新生命周期以及决策风险和学习管理--的理解。ISO 15288 流程框架及其在 INCOSE 系统工程手册(2015 年)中的阐述描述了系统开发和其他生命周期流程。敏捷 "系统工程方法在一定程度上解决了在动态、不确定和不断变化的环境中提高流程性能的问题。这两种方法通常都是用业务流程的程序语言来描述的,因此并不总是很清楚不同的方法从根本上是相悖的,还是只是同一枚硬币的不同面。使用动态系统模型来描述目标系统、环境和生命周期管理流程,可以让我们将早期的技术工具(如噪声环境下的最优控制理论)应用到新兴的创新方法中。
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Innovation, Risk, Agility, and Learning, Viewed as Optimal Control and Estimation

This paper summarizes how a well-understood problem—optimal control and estimation in “noisy” environments—provides a framework to advance understanding of a well-known but less well-mastered problem—system innovation life cycles and management of decision risks and learning. The ISO15288 process framework and its exposition in the INCOSE Systems Engineering Handbook (2015) describe system development and other life cycle processes. Concerns about improving the performance of processes in dynamic, uncertain, and changing environments are partly addressed by “agile” systems engineering approaches. Both are typically described in the procedural language of business processes, so it is not always clear whether the different approaches are fundamentally at odds, or just different sides of the same coin. Describing the target system, its environment, and the life cycle management processes using models of dynamical systems allows us to apply earlier technical tools, such as the theory of optimal control in noisy environments, to emerging innovation methods.

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来源期刊
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
期刊最新文献
ISSUE INFORMATION Innovation Ecosystem Dynamics, Value and Learning I: What Can Hamilton Tell Us? Realizing the Promise of Digital Engineering: Planning, Implementing, and Evolving the Ecosystem Requirements Statements Are Transfer Functions: An Insight from Model-Based Systems Engineering Feelings and Physics: Emotional, Psychological, and Other Soft Human Requirements, by Model-Based Systems Engineering
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