NYI: Using Fuzzy Logic to Deal with Qualitive Requirements and Uncertaintly in the Environment

NYI：使用模糊逻辑处理环境中的定性要求和不确定性

• 批准号: 9257293
• 负责人: John Yen
• 金额: $ 31.6万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9257293&HistoricalAwards=false
• 关键词: NYI; Using; Fuzzy; Logic; Deal

Intelligent systems often need to deal with two kinds of uncertainty: (1) system requirements that are qualitative in nature, and (2) uncertainty about the state of the external environment. The primary objective of this research is to develop sound and practical techniques for dealing with these issues. To address the first issue, fuzzy logic based methodologies for specifying and validating qualitative requirements are being developed. Explicitly capturing the elasticity of the system's requirements facilitates the exploration of various trade-offs during the design stage and enables a more realistic validation of the implemented system. To address the second issue, systematic modeling techniques for designing hybrid autonomous intelligent systems are being developed. These techniques use fuzzy logic to integrate AI symbolic problem solving with the numeric processing exhibited by neural networks and model-based control. Potential industrial applications of such hybrid systems range from the petrochemical process control to autonomous vehicle systems and automated manufacturing systems. Methodologies and techniques developed in this research project will not only enhance the quality and the adaptability of the next generation of intelligent systems, but will also reduce the cost for designing and maintaining them.

智能系统通常需要处理两种不确定性：（1）本质上是定性的系统需求，（2）外部环境状态的不确定性。这项研究的主要目标是开发解决这些问题的合理且实用的技术。为了解决第一个问题，正在开发基于模糊逻辑的方法来指定和验证定性要求。明确捕捉系统需求的弹性有助于在设计阶段探索各种权衡，并能够对已实施的系统进行更现实的验证。为了解决第二个问题，正在开发用于设计混合自主智能系统的系统建模技术。这些技术使用模糊逻辑将人工智能符号问题解决与神经网络和基于模型的控制所展示的数字处理相结合。这种混合系统的潜在工业应用范围从石化过程控制到自动驾驶车辆系统和自动化制造系统。该研究项目开发的方法和技术不仅将提高下一代智能系统的质量和适应性，而且还将降低设计和维护它们的成本。